Optimizing Hive Queries

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  • Optimizing Hive Queries

    41:35

    Apache Hive is a data warehousing system for large volumes of data stored in Hadoop. However, the data is useless unless you can use it to add value to your company. Hive provides a SQL-based query language that dramatically simplifies the process of querying your large data sets. That is especially important while your data scientists are developing and refining their queries to improve their understanding of the data. In many companies, such as Facebook, Hive accounts for a large percentage of the total MapReduce queries that are run on the system. Although Hive makes writing large data queries easier for the user, there are many performance traps for the unwary. Many of them are artifacts of the way Hive has evolved over the years and the requirement that the default behavior must be safe for all users. This talk will present examples of how Hive users have made mistakes that made their queries run much much longer than necessary. It will also present guidelines for how to get better performance for your queries and how to look at the query plan to understand what Hive is doing.

  • Apache Hive Cost based optimization Performance Testing

    6:03

    The main goal of a CBO is to generate efficient execution plans by examining the tables and conditions specified in the query, ultimately cutting down on query execution time and reducing resource utilization. Calcite has an efficient plan pruner that can select the cheapest query plan. All SQL queries are converted by Hive to a physical operator tree, optimized and converted to Tez/MapReduce jobs, then executed on the Hadoop cluster. This conversion includes SQL parsing and transforming, as well as operator-tree optimization.


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  • HIVE Best Practices

    54:05

    Dean Wampler, Ph.D., Principal Consultant at Think Big Analytics and the co-author of Programming Hive, will discuss several Hive techniques for managing your data effectively and optimizing the performance of your queries.

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  • Data Warehouse using Hadoop eco system - 04 Hive Performance Tuning - Strategies

    13:05

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    ORC File & Vectorization - Improving Hive Data Storage and Query Performance

    40:15

    Hive's RCFile has been the standard format for storing Hive data for the last 3 years. However, RCFile has limitations because it treats each column as a binary blob without semantics. The upcoming Hive 0.11 will add a new file format named Optimized Row Columnar (ORC) file that uses and retains the type information from the table definition. ORC uses type specific readers and writers that provide light weight compression techniques such as dictionary encoding, bit packing, delta encoding, and run length encoding — resulting in dramatically smaller files. Additionally, ORC can apply generic compression using zlib, LZO, or Snappy on top of the lightweight compression for even smaller files. However, storage savings are only part of the gain. ORC supports projection, which selects subsets of the columns for reading, so that queries reading only one column read only the required bytes. Furthermore, ORC files include light weight indexes that include the minimum and maximum values for each column in each set of 10,000 rows and the entire file. Using pushdown filters from Hive, the file reader can skip entire sets of rows that aren't important for this query.
    Columnar storage formats like ORC reduce I/O and storage use, but it's just as important to reduce CPU usage. A technical breakthrough called vectorized query execution works nicely with column store formats to do this. Vectorized query execution has proven to give dramatic performance speedups, on the order of 10X to 100X, for structured data processing. We describe how we're adding vectorized query execution to Hive, coupling it with ORC with a vectorized iterator.

  • 0604 Cost Based Query Optimization in Hive

    40:16

  • Apache Hive - Hive joins, execution engines and explain/execution plan

    13:52

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  • Innovations In Apache Hadoop MapReduce, Pig and Hive for Improving Query Performance

    43:51

    Apache Hadoop and its ecosystem projects Hive and Pig support interactions with data sets of enormous sizes. Petabyte scale data warehouse infrastructures are built on top of Hadoop for providing access to data of massive and small sizes. Hadoop always excelled at large-scale data processing; however, running smaller queries has been problematic due to the batch-oriented nature of the system. With the advent of Hadoop YARN which is a far more general purpose system, we have made tremendous improvements to Hadoop MapReduce. Taken together, the enhancements we have made to the resource management system (YARN), to MapReduce framework and to Hive and Pig themselves, we are elevating the Hadoop ecosystem to be much more powerful, performant and user-friendly. This talk will cover the improvements we have made to YARN, MapReduce, Pig and Hive. We will also walk through the future enhancements we have planned.

  • Running Hive Queries on Tez, Execution of Pig on Tez on Hortonworks Data Platform

    1:55:58

    Running realtime demo on Hive optimization of Hortonworks data platform(HDP), Hive-QL on Tez, CBO process, vectorization, execution of Apache Pig on Tez on Horton works Data Platform 2.1

  • Optimizing Hadoop using Microsoft Azure HDInsight

    2:11:56

    In this webinar, end to end demonstrations are given on optimizing Big Data Hadoop using Microsoft Azure HDInsight. Demos like Clickstream analytics, optimizing Hive queries using Apache Tez on HDP on HDInsight, Springer with SQL server 2016 are discussed. Realtime visuals with Web log ClickStream Analytics using PowerBI are presented.

  • Catalyst: A Functional Query Optimizer for Spark and Shark

    43:46

    Shark is a SQL engine built on Apache Hive, that replaces Hive's MapReduce execution engine with Apache Spark. Spark's fine-grained resource model and efficient execution engine allow Shark to outperform Hive by over 100x for data stored in memory. However, until now, Shark's performance has been limited by the flexibility of Hive's query optimizer. Catalyst aims to remedy this situation by building a simple yet powerful optimization framework using Scala language features.
    Query optimization can greatly improve both the productivity of developers and the performance of the queries that they write. A good query optimizer is capable of automatically rewriting relational queries to execute more efficiently, using techniques such as filtering data early, utilizing available indexes, and even ensuring different data sources are joined in the most efficient order. By performing these transformations, the optimizer not only improves the execution times of relational queries, but also frees the developer to focus on the semantics of their application instead of its performance.
    Unfortunately, building an optimizer is an incredibly complex engineering task and thus many open source systems perform only very simple optimizations. Past research [1,2] has attempted to combat this complexity by providing frameworks that allow the creators of optimizers to write possible optimizations as a set of declarative rules. However, the use of such frameworks has required the creation and maintenance of special optimizer compilers and forced the burden of learning a complex domain specific language upon those wishing to add features to the optimizer.
    Catalyst solves this problem by leveraging Scala's powerful pattern matching and runtime reflection. This framework allows developers to concisely specify complex optimizations, such as pushing filters past joins functionally. Increased conciseness allows our developers both to create new optimizations faster and more easily reason about the correctness of the optimization.
    Catalyst also uses the new reflection capabilities in Scala 2.10 to generate custom classes at runtime for storing intermediate results and evaluating complex relational expressions. Doing so allows us to avoid boxing of primitive values and has been shown to improve performance by orders of magnitude in some cases.
    [1] Graefe, G. The Cascades Framework for Query Optimization. In Data Engineering Bulletin. Sept. 1995.[2] Goetz Graefe , David J. DeWitt, The EXODUS optimizer generator, Proceedings of the 1987 ACM SIGMOD international conference on Management of data, p.160-172, May 27-29, 1987, San Francisco, California, United States


    Author:
    Michael Armbrust
    Software Engineer at Databricks, interested in distributed databases, query languages, scala, and more.

  • Apache Hive - 01 Write and Execute a Hive Query

    11:11

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  • HIVE ORC TABLE from Non orc Table

    8:09

    ORC stands for Optimized Row Columnar Format.
    It is used to achieve higher compression rate and better query optimization.
    Its very easy to create ORC table from existing NON-ORC table that has already Data in it.
    We will see how to practice this with step by step instructions.

  • Optimizing Streaming SQL Queries -- Julian Hyde , 2/17/16

    59:21

    Optimizing Streaming SQL Queries by Julian Hyde (Hortonworks)

    Synopsis:What is SamzaSQL, and what might I use it for? Does this mean that Samza is turning into a database? What is a query optimizer, and what can it do for my streaming queries?

    Bio: Julian Hyde is an expert in query optimization, in-memory analytics, and streaming. He is PMC chair of Apache Calcite, the query planning framework behind Hive, Drill, Kylin and Phoenix. He was the original developer of the Mondrian OLAP engine, and is an architect at Hortonworks.

  • Apache Hive - Hive Sub Queries and Total Ordering

    10:31

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  • Hive Query Language Basics

    55:44

  • Hive Explain Plan and Stats

    10:20

  • HiveQL-Data Manipulation - Hive Query Language - Loading Data in Hive Tables

    15:18

    Learn HiveQL-Data Manipulation by Easylearning guru describe you about How Hive Query Language Manipulated Data and Loading Data in Hive Tables. Subscribe our channel for updates or visit: for more detail.

  • Hive Tutorial 1 | Hive Tutorial for Beginners | Understanding Hive In Depth | Edureka

    2:22:58

    Check out our Hive Tutorial blog series:
    This Hive tutorial gives in-depth knowledge on Apache Hive. Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive structures data into well-understood database concepts such as tables, rows, columns and partitions.

    Check our complete Hadoop playlist here:

    #HiveTutorial #ApacheHiveTutorial #HiveTutorialForBeginners

    The video talks about the following points:

    1. What is Hive?
    2. Why to use Hive?
    3. Where to use hive and not Pig?
    4. Hive Architecture
    5. Hive Components
    6. How Facebook Uses hive?
    7. Hive vs RDBMS
    8. Limitations of hive
    9. Hive Types
    10. Hive commands and Hive queries

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    Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you:

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    4. Learn data loading techniques using Sqoop and Flume
    5. Perform data analytics using Pig, Hive and YARN
    6. Implement HBase and MapReduce integration
    7. Implement Advanced Usage and Indexing
    8. Schedule jobs using Oozie
    9. Implement best practices for Hadoop development
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    If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career:
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    Big Data! A Worldwide Problem?

    According to Wikipedia, Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success!

    The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data.

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    Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself!

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  • Hadoop Tutorial - Hue: Execute Hive queries and schedule them with Oozie

    6:24

    In the previous episode ( we saw how to to transfer some file data into Hadoop. In order to interrogate easily the data, the next step is to create some Hive tables. This will enable quick interaction with high level languages like SQL and Pig.
    We experiment with the SQL queries, then parameterize them and insert them into a workflow in order to run them together in parallel. Including Hive queries in an Oozie workflow is a pretty common use case with recurrent pitfalls as seen on the user group. We can do it with Hue in a few clicks.
    gethue.com

  • Hadoop Tutorial for Beginners - 34 Hive Optimization Techniques

    9:03

    In this tutorial you will learn about Hive Optimization Techniques

  • Optimizing Hive on Azure HDInsight

    1:1:54

    HDInsight allows you to run Big Data technologies (including Hadoop) on Microsoft Azure. If you have a Hadoop cluster, more than likely you use Hive in some capacity. Hive is the SQL engine on Hadoop and is mature, scalable, and heavily used in production scenarios. Hive can run different types of workloads including ETL, reporting, data mining and others. Each of these workloads needs to be tuned to get the best performance. At this session you will learn how to optimize your system better. We will discuss performance optimization at both an architecture layer and at the execution engine layer. Come prepared for a hands-on view of HDInsight including demos.

  • Cloudera Impala with R Integration

    28:59

    1. What is Cloudera Impala?
    2. What is Hive?
    3. Differences of Impala from Relational Databases and Hive.
    4. What is R and RStudio?
    5. Amazon EC2 cluster with preinstalled Hadoop, Hive and Impala
    6. Loading sample Test data in HDFS format on ec2.
    7. Writing SQL queries and executing it against the data loaded in Impala and Hive.
    8. Optimizing queries in Impala
    9. Configuring Cloudera Impala ODBC drivers
    10. Analyzing Hadoop data set with R and Impala.
    11. Plotting the Impala dataset as a graph on Rstudio for analytics.

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    LLAP sub Second Analytical Queries in Hive

    35:08

    Hortonworks

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    Using Spark and Hive - PART 1: Spark as ETL tool

    7:00

    Working with Spark and Hive

    Part 1: Scenario - Spark as ETL tool
    Write to Parquet file using Spark

    Part 2: SparkSQL to query data from Hive
    Read Hive table data from Spark

    Create an External Table
    Query the data from Hive
    Add new data
    Query the data from Hive




    case class Person(name: String, age: Int, sex:String)
    val data = Seq(Person(Jack, 25,M), Person(Jill, 25,F), Person(Jess, 24,F))

    val df = data.toDF()

    import org.apache.spark.sql.SaveMode
    df.select(name, age, sex).write.mode(SaveMode.Append).format(parquet).save(/tmp/person)

    //Add new data
    val data = Seq(Person(John, 25,M))
    val df = data.toDF()
    df.select(name, age, sex).write.mode(SaveMode.Append).format(parquet).save(/tmp/person)


    CREATE EXTERNAL TABLE person ( name String, age Int, sex String)
    STORED as PARQUET
    LOCATION '/tmp/person'

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    Analytical Queries with Hive SQL Windowing and Table Functions

    37:15

    Speaker: Harish Butani, SAP

  • desc

    Hive Inner Join, Right Outer Join, Map side Join

    33:03

    In this video I am explain about Hive Inner Join, Right Outer Join, Map Join. How to do it what is the process to join.

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    One Table: Big SQL tables ARE Hive Tables - Big SQL just queries them WAY faster

    1:22

    This video is just a quick demonstration of how Big SQL tables are really Hive tables. I show you how to create a table in Big SQL, create some data, and then immediately query the same table from Hive. This is proof that Big SQL integrates with Hive metastore and uses Hive Tables.

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    03 - Getting Started with Microsoft Big Data - Introduction to Hive and HiveQL

    1:6:20

    3- In this module, you will learn how to leverage your SQL skills by using Hive and HiveQL to create tables and views and run queries on top of Hadoop data using an HDInsight cluster.

  • Hadoop Training 2 : Deep Dive In HDFS | What is HDFS ? | What is Hive ?

    48:52

    Full Hadoop Training is in Just $69/3500INR visit : HadoopExam.com

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    Please find the link for Hadoop Interview Questions PDF


    Module 1 : Introduction to BigData, Hadoop (HDFS and MapReduce) : Available (Length 35 Minutes)
    1. BigData Inroduction
    2. Hadoop Introduction
    3. HDFS Introduction
    4. MapReduce Introduction

    Video URL :

    Module 2 : Deep Dive in HDFS : Available (Length 48 Minutes)


    1. HDFS Design
    2. Fundamental of HDFS (Blocks, NameNode, DataNode, Secondary Name Node)
    3. Rack Awareness
    4. Read/Write from HDFS
    5. HDFS Federation and High Availability
    6. Parallel Copying using DistCp
    7. HDFS Command Line Interface
    Video URL :

    Module 3 : Understanding MapReduce
    1. JobTracker and TaskTracker
    2. Topology Hadoop cluster
    3. Example of MapReduce
    Map Function
    Reduce Function
    4. Java Implementation of MapReduce
    5. DataFlow of MapReduce
    6. Use of Combiner

    Video URL : Watch Private Video

    Module 4 : MapReduce Internals -1 (In Detail) : Available (Length 57 Minutes)

    1. How MapReduce Works
    2. Anatomy of MapReduce Job (MR-1)
    3. Submission & Initialization of MapReduce Job (What Happen ?)
    4. Assigning & Execution of Tasks
    5. Monitoring & Progress of MapReduce Job
    6. Completion of Job
    7. Handling of MapReduce Job
    - Task Failure
    - TaskTracker Failure
    - JobTracker Failure

    Video URL : Watch Private Video

    Module 5 : MapReduce-2 (YARN : Yet Another Resource Negotiator) : Available (Length 52 Minutes)


    1. Limitation of Current Architecture (Classic)
    2. What are the Requirement ?
    3. YARN Architecture
    4. JobSubmission and Job Initialization
    5. Task Assignment and Task Execution
    6. Progress and Monitoring of the Job
    7. Failure Handling in YARN
    - Task Failure
    - Application Master Failure
    - Node Manager Failure
    - Resource Manager Failure

    Video URL : Watch Private Video

    Module 6 : Advanced Topic for MapReduce (Performance and Optimization) : Available (Length 58 Minutes)

    1. Job Sceduling
    2. In Depth Shuffle and Sorting
    3. Speculative Execution
    4. Output Committers
    5. JVM Reuse in MR1
    6. Configuration and Performance Tuning

    Video URL : Watch Private Video

    Module 7 : Advanced MapReduce Algorithm : Available

    File Based Data Structure
    - Sequence File
    - MapFile
    Default Sorting In MapReduce
    - Data Filtering (Map-only jobs)
    - Partial Sorting
    Data Lookup Stratgies
    - In MapFiles
    Sorting Algorithm
    - Total Sort (Globally Sorted Data)
    - InputSampler
    - Secondary Sort

    Video URL : Watch Private Video
    Module 8 : Advanced MapReduce Algorithm -2 : Available

    1. MapReduce Joining
    - Reduce Side Join
    - MapSide Join
    - Semi Join
    2. MapReduce Job Chaining
    - MapReduce Sequence Chaining
    - MapReduce Complex Chaining

    Module 9 : Features of MapReduce : Available

    MapReduce Counters
    Data Distribution
    Using JobConfiguration
    Distributed Cache

    Module 11 : Apache Pig : Available (Length 52 Minutes)

    1. What is Pig ?
    2. Introduction to Pig Data Flow Engine
    3. Pig and MapReduce in Detail
    4. When should Pig Used ?
    5. Pig and Hadoop Cluster


    Video URL : Watch Private Video

    Module 12 : Fundamental of Apache Hive Part-1 : Available (Length 60 Minutes)

    1. What is Hive ?
    2. Architecture of Hive
    3. Hive Services
    4. Hive Clients
    5. how Hive Differs from Traditional RDBMS
    6. Introduction to HiveQL
    7. Data Types and File Formats in Hive
    8. File Encoding
    9. Common problems while working with Hive

    Module 13 : Apache Hive : Available (Length 73 Minutes )
    1. HiveQL
    2. Managed and External Tables
    3. Understand Storage Formats
    4. Querying Data
    - Sorting and Aggregation
    - MapReduce In Query
    - Joins, SubQueries and Views
    5. Writing User Defined Functions (UDFs)

    Module 14 : Single Node Hadoop Cluster Set Up In Amazon Cloud : Available (Length 60 Minutes Hands On Practice Session)
    1. � How to create instance on Amazon EC2
    2. � How to connect that Instance Using putty
    3. � Installing Hadoop framework on this instance
    4. � Run sample wordcount example which come with Hadoop framework.
    In 30 minutes you can create Hadoop Single Node Cluster in Amazon cloud, does it interest you ?


    Module 15 : Hands On : Implementation of NGram algorithm : Available (Length 48 Minutes Hands On Practice Session)
    1. Understand the NGram concept using (Google Books NGram )
    2. Step by Step Process creating and Configuring eclipse for writing MapReduce Code
    3. Deploying the NGram application in Hadoop Installed in Amazon EC2
    4. Analyzing the Result by Running NGram application (UniGram, BiGram, TriGram etc.)

    Hadoop Learning Resources
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    HadoopExam.com

  • Apache Hive - Create Hive Bucketed Table

    11:30

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  • Differences between Hive, Tez, Impala and Spark Sql

    23:41

    This hangout is to cover difference between different execution engines available in Hadoop and Spark clusters

  • HIVE BUCKETING 11

    56:48

  • #bbuzz 2015: Szehon Ho - Hive on Spark

    33:38

    Find more information here:

    Apache Hive is a popular SQL interface for batch processing and ETL using Apache Hadoop. Until recently, MapReduce was the only Hadoop execution engine for Hive queries. But today, alternative execution engines are available — such as Apache Spark and Apache Tez. The Hive and Spark communities are joining forces to introduce Spark as a new execution engine option for Hive.

    In this talk we'll discuss the Hive on Spark project. Topics include the motivations, such as improving Hive user experience and streamlining operational management for Spark shops, some background and comparisons of MapRededuce and Spark, and the technical process of porting a complex real-world application from MapReduce to Spark. A demo will also be presented.

  • Chapter 3 Hive Queries using Excel

    5:16

    Using the Hive Panel in Excel in order to send queries to HDInsight, Microsoft's distro of Hadoop

  • How to work with partitions in hive

    15:06

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    Whats New in the Berkeley Data Analytics Stack

    40:02

    The Berkeley Data Analytics Stack (BDAS) aims to address emerging challenges in data analysis through a set of systems, including Spark, Shark and Mesos, that enable faster and more powerful analytics. In this talk, we'll cover two recent additions to BDAS:
    * Spark Streaming is an extension of Spark that enables high-speed, fault-tolerant stream processing through a high-level API. It uses a new processing model called discretized streams to enable fault-tolerant stateful processing with exactly-once semantics, without the costly transactions required by existing systems. This lets applications process much higher rates of data per node. It also makes programming streaming applications easier by providing a set of high-level operators on streams (e.g. maps, filters, and windows) in Java and Scala.
    * Shark is a Spark-based data warehouse system compatible with Hive. It can answer Hive QL queries up to 100 times faster than Hive without modification to existing data or queries. Shark supports Hive's query language, metastore, serialization formats, and user-defined functions. It employs a number of novel and traditional database optimization techniques, including column-oriented storage and mid-query replanning, to efficiently execute SQL on top of Spark. The system is in early use at companies including Yahoo! and Conviva.

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    Hadoop Training 1 : Introduction to BigData, Hadoop, HDFS, MAPReduce HadoopExam.com

    34:34

    Full Hadoop Training is in Just $60/3000INR visit : HadoopExam.com

    Download full training Brochure from :

    Please find the link for Hadoop Interview Questions PDF


    Big Data and Hadoop Trainings are Being Used by Learners from US, UK , Europe , Spain, Germany, Singapore, Malaysia, Egypt, Saudi Arabia, Turkey , Dubai, India, Chicago , MA, etc

    Module 1 : Introduction to BigData, Hadoop (HDFS and MapReduce) : Available (Length 35 Minutes)
    1. BigData Inroduction
    2. Hadoop Introduction
    3. HDFS Introduction
    4. MapReduce Introduction

    Video URL :

    Module 2 : Deep Dive in HDFS : Available (Length 48 Minutes)


    1. HDFS Design
    2. Fundamental of HDFS
    3. Rack Awareness
    4. Read/Write from HDFS
    5. HDFS Federation and High Availability
    6. Parallel Copying using DistCp
    7. HDFS Command Line Interface
    Video URL :

    Module 3 : Understanding MapReduce
    1. JobTracker and TaskTracker
    2. Topology Hadoop cluster
    3. Example of MapReduce
    Map Function
    Reduce Function
    4. Java Implementation of MapReduce
    5. DataFlow of MapReduce
    6. Use of Combiner

    Video URL : Watch Private Video

    Module 4 : MapReduce Internals -1 (In Detail)

    1. How MapReduce Works
    2. Anatomy of MapReduce Job (MR-1)
    3. Submission & Initialization of MapReduce Job (What Happen ?)
    4. Assigning & Execution of Tasks
    5. Monitoring & Progress of MapReduce Job
    6. Completion of Job
    7. Handling of MapReduce Job
    - Task Failure
    - TaskTracker Failure
    - JobTracker Failure

    Video URL : Watch Private Video

    Module 5 : MapReduce-2 (YARN : Yet Another Resource Negotiator) :

    1. Limitation of Current Architecture (Classic)
    2. What are the Requirement ?
    3. YARN Architecture
    4. JobSubmission and Job Initialization
    5. Task Assignment and Task Execution
    6. Progress and Monitoring of the Job
    7. Failure Handling in YARN
    - Task Failure
    - Application Master Failure
    - Node Manager Failure
    - Resource Manager Failure

    Video URL : Watch Private Video

    Module 6 : Advanced Topic for MapReduce (Performance and Optimization)

    1. Job Sceduling
    2. In Depth Shuffle and Sorting
    3. Speculative Execution
    4. Output Committers
    5. JVM Reuse in MR1
    6. Configuration and Performance Tuning

    Video URL : Watch Private Video

    Module 7 : Advanced MapReduce Algorithm : Available (Length 87 Minutes)

    File Based Data Structure
    - Sequence File
    - MapFile
    Default Sorting In MapReduce
    - Data Filtering (Map-only jobs)
    - Partial Sorting
    Data Lookup Stratgies
    - In MapFiles
    Sorting Algorithm
    - Total Sort (Globally Sorted Data)
    - InputSampler
    - Secondary Sort

    Video URL : Watch Private Video
    Module 8 : Advanced MapReduce Algorithm -2

    1. MapReduce Joining
    - Reduce Side Join
    - MapSide Join
    - Semi Join
    2. MapReduce Job Chaining
    - MapReduce Sequence Chaining
    - MapReduce Complex Chaining

    Module 9 : Features of MapReduce : Available

    Introduction to MapReduce Counters
    Data Distribution
    Using JobConfiguration
    Distributed Cache

    Module 11 : Apache Pig : Available (Length 52 Minutes)

    1. What is Pig ?
    2. Introduction to Pig Data Flow Engine
    3. Pig and MapReduce in Detail
    4. When should Pig Used ?
    5. Pig and Hadoop Cluster


    Video URL : Watch Private Video

    Module 12 : Fundamental of Apache Hive Part-1 : Available (Length 60 Minutes)

    1. What is Hive ?
    2. Architecture of Hive
    3. Hive Services
    4. Hive Clients
    5. how Hive Differs from Traditional RDBMS
    6. Introduction to HiveQL
    7. Data Types and File Formats in Hive
    8. File Encoding
    9. Common problems while working with Hive

    Module 13 : Apache Hive : Available (Length 73 Minutes )
    1. HiveQL
    2. Managed and External Tables
    3. Understand Storage Formats
    4. Querying Data
    - Sorting and Aggregation
    - MapReduce In Query
    - Joins, SubQueries and Views
    5. Writing User Defined Functions (UDFs)

    Module 14 : Single Node Hadoop Cluster Set Up In Amazon Cloud : Available (Length 60 Minutes Hands On Practice Session)
    1. � How to create instance on Amazon EC2
    2. � How to connect that Instance Using putty
    3. � Installing Hadoop framework on this instance
    4. � Run sample wordcount example which come with Hadoop framework.
    In 30 minutes you can create Hadoop Single Node Cluster in Amazon cloud, does it interest you ?


    Module 15 : Hands On : Implementation of NGram algorithm : Available (Length 48 Minutes Hands On Practice Session)
    1. Understand the NGram concept using (Google Books NGram )
    2. Step by Step Process creating and Configuring eclipse for writing MapReduce Code
    3. Deploying the NGram application in Hadoop Installed in Amazon EC2
    4. Analyzing the Result by Running NGram application (UniGram, BiGram, TriGram etc.)

    Hadoop Learning Resources
    Phone : 022-42669636
    Mobile : +91-8879712614
    HadoopExam.com

  • desc

    Bucket Your Partitions Wisely | Cassandra Summit 2016

    35:22

    Slides: | When we talk about bucketing we essentially talk about possibilities to split cassandra partitions in several smaller parts, rather than having only one large partition.

    Bucketing of cassandra partitions can be crucial for optimizing queries, preventing large partitions or to fight TombstoneOverwhelmingException which can occur when creating too many tombstones.

    In this talk I want to show how to recognize large partitions during datamodeling. I will also show different strategies we used in our projects to create, use and maintain buckets for our partitions.

    About the Speaker
    Markus Hofer IT Consultant, codecentric AG

    Markus Hofer works as an IT Consultant for codecentric AG in Minster, Germany. He works on microservice architectures backed by DSE and/or Apache Cassandra. Markus supports and trains customers building cassandra based applications.

  • Big Data Hadoop training - HDFS JAVA API Class by [email protected] 848-200-0448

    32:29

    Big Data Hadoop training provided Online from USA industry expert trainers with real time project experience.
    CONTACT: 848-200-0448(or) Email - [email protected]

    ELearningLine.com provides big data Hadoop training courses primarily for the people who just want to establish and boost their career in the field of Big Data using Hadoop Framework. The scope of Big Data Hadoop developer is in a growing trend in the recent days.



    ********************************************************************

    1. Motivation For Hadoop
    Problems with traditional large-scale systems
    Requirements for a new approach
    Introducing Hadoop
    2. Hadoop: Basic Concepts
    The Hadoop Project and Hadoop Components
    The Hadoop Distributed File System
    Hands-On Exercise: Using HDFS
    How MapReduce Works
    Hands-On Exercise: Running a MapReduce Job
    How a Hadoop Cluster Operates
    Other Hadoop Ecosystem Projects
    3. Writing a MapReduce Program
    The MapReduce Flow
    Basic MapReduce API Concepts
    Writing MapReduce Drivers, Mappers and Reducers in Java
    Hands-On Exercise: Writing a MapReduce Program
    Differences Between the Old and New MapReduce APIs
    4. Unit Testing MapReduce Programs
    Unit Testing
    The JUnit and MRUnit Testing Frameworks
    Writing Unit Tests with MRUnit
    Hands-On Exercise: Writing Unit Tests with the MRUnit Framework
    5. Delving Deeper into the Hadoop API
    Using the ToolRunner Class
    Hands-On Exercise: Writing and Implementing a Combiner
    Setting Up and Tearing Down Mappers and Reducers by Using the Configure and Close Methods
    Writing Custom Partitioners for Better Load Balancing
    Optional Hands-On Exercise: Writing a Partitioner
    Accessing HDFS Programmatically
    Using The Distributed Cache
    Using the Hadoop API’s Library of Mappers, Reducers and Partitioners JSON, Ajax and PHP
    6. Development Tips and Techniques
    Strategies for Debugging MapReduce Code
    Testing MapReduce Code Locally by Using LocalJobReducer
    Writing and Viewing Log Files
    Retrieving Job Information with Counters
    Determining the Optimal Number of Reducers for a Job
    Creating Map-Only MapReduce Jobs
    Hands-On Exercise: Using Counters and a Map-Only Job
    7. Data Input and Output
    Creating Custom Writable and WritableComparable Implementations
    Saving Binary Data Using SequenceFile and Avro Data Files
    Implementing Custom Input Formats and Output Formats
    Issues to Consider When Using File Compression
    Hands-On Exercise: Using SequenceFiles and File Compression
    8. Common MapReduce Algorithms
    Sorting and Searching Large Data Sets
    Performing a Secondary Sort
    Indexing Data
    Hands-On Exercise: Creating an Inverted Index
    Computing Term Frequency — Inverse Document Frequency
    Calculating Word Co-Occurrence
    Hands-On Exercise: Calculating Word Co-Occurrence (Optional)
    Hands-On Exercise: Implementing Word Co-Occurrence with a Customer WritableComparable (Optional)
    9. Joining Data Sets in MapReduce Jobs
    Writing a Map-Side Join
    Writing a Reduce-Side Join
    Integrating Hadoop into the Enterprise Workflow
    Integrating Hadoop into an Existing Enterprise
    Loading Data from an RDBMS into HDFS by Using Sqoop
    Hands-On Exercise: Importing Data with Sqoop
    Managing Real-Time Data Using Flume
    Accessing HDFS from Legacy Systems with FuseDFS and HttpFS
    10. Pig
    Introduction
    Installing and Running Pig
    Downloading and Installing Pig
    Running Pig
    Grunt
    Interpreting Pig Latin Scripts
    HDFS Commands
    Controlling Pig
    The Pig Data Model
    Data Types
    Schemas
    Basic Pig Latin
    Input and Output
    Relational Operations
    User Defined Functions
    Advanced Pig Latin
    Advanced Relational Operations
    Using Pig with Legacy Code
    Integrating Pig and MapReduce
    Nonlinear Data Flows
    Controlling Execution
    Pig Latin Preprocessor
    Developing and Testing Scripts
    Development Tools
    Testing Your Scripts with PigUnit
    11. HIVE
    Introduction
    Getting started with HIVE
    Data Types and file formats
    HIVEQL – Data definition
    HIVEQL – Data manipulation
    HIVEQL – Queries
    HIVEQL – Views
    HIVEQL – Indexes
    Schema Design
    Tuning
    12. Hbase
    HBase architecture
    HBase versions and origins
    HBase vs. RDBMS
    HBase Master and Region Servers
    Intro to ZooKeeper
    Data Modeling
    Column Families and Regions
    HBase Architecture Detailed
    Developing with HBase
    Schema Design
    Schema Design Best Practices
    HBase and Hive Integration
    HBase Performance Optimization
    13. Apache Flume and Chukawa
    Apache Flume introduction
    Flume architecture
    Flume use cases
    Apache chukawa introduction
    Chukwa Architecture
    Chukawa use cases
    14. Apache Oozie
    Apache oozie introduction
    Installation and configuration
    Oozie use cases
    15. API (Application programming interface)
    HDFS API
    PIG API
    HBASE API
    HIVE API
    16. NoSQL
    Introduction
    MongoDB and MapReduce
    Cassendra and MapReduce

  • Analyzing Genomic Data at Scale on AWS with Station Xs GenePool

    5:18

    Learn more -

    On the next This Is My Architecture, Anish from Station X explains how they built a platform to analyze, visualize and manage genomic information at scale on AWS. You’ll learn how they use Qubole to manage clusters running Presto and Hive that power their interactive, near real-time query interface. You’ll also learn how they optimize query performance by storing their data in ORC stripes and sorting by genomic coordinate.

  • BIG DATA HADOOP Tutorial by ELearningLine @ 848-200-0448

    54:17

    For more information about Big Data Hadoop tutorial and please visit: || Call us: 848-200-0448 || Email us - [email protected]
    ---------------------------------------------------------------------------------------------------------
    ELearningLine provides big data Hadoop training courses primarily for the people who just want to establish and boost their career in the field of Big Data using Hadoop Framework. The scope of Big Data Hadoop developer is in a growing trend in the recent days.

    Curriculum:

    MODULE 1. Motivation For Hadoop
    Problems with traditional large-scale systems, Requirements for a new approach, Introducing Hadoop

    MODULE 2. Hadoop: Basic Concepts
    The Hadoop Project and Hadoop Components, The Hadoop Distributed File System, Hands-On Exercise: Using HDFS, How MapReduce Works, Hands-On Exercise: Running a MapReduce Job, How a Hadoop Cluster Operates, Other Hadoop Ecosystem Projects

    MODULE 3. Writing a MapReduce Program
    The MapReduce Flow, Basic MapReduce API Concepts, Writing MapReduce Drivers, Mappers and Reducers in Java, Hands-On Exercise: Writing a MapReduce Program, Differences Between the Old and New MapReduce APIs

    MODULE 4. Unit Testing MapReduce Programs
    Unit Testing, The JUnit and MRUnit Testing Frameworks, Writing Unit Tests with MRUnit, Hands-On Exercise: Writing Unit Tests with the MRUnit Framework

    MODULE 5. Delving Deeper into the Hadoop API
    Using the ToolRunner Class, Hands-On Exercise: Writing and Implementing a Combiner, Setting Up and Tearing Down Mappers and Reducers by Using the Configure and Close Methods, Writing Custom Partitioners for Better Load Balancing, Optional Hands-On Exercise: Writing a Partitioner, Accessing HDFS Programmatically, Using The Distributed Cache, Using the Hadoop API’s Library of Mappers, Reducers and Partitioners JSON, Ajax and PHP

    MODULE 6. Development Tips and Techniques
    Strategies for Debugging MapReduce Code, Testing MapReduce Code Locally by Using LocalJobReducer, Writing and Viewing Log FilesRetrieving Job Information with Counters
    Determining the Optimal Number of Reducers for a Job
    Creating Map-Only MapReduce Jobs
    Hands-On Exercise: Using Counters and a Map-Only Job

    MODULE 7. Data Input and Output
    Creating Custom Writable and WritableComparable Implementations, Saving Binary Data Using SequenceFile and Avro Data Files, Implementing Custom Input Formats and Output Formats, Issues to Consider When Using File Compression, Hands-On Exercise: Using SequenceFiles and File Compression

    MODULE 8. Common MapReduce Algorithms
    Sorting and Searching Large Data Sets, Performing a Secondary Sort, Indexing Data, Hands-On Exercise: Creating an Inverted Index, Computing Term Frequency — Inverse Document Frequency, Calculating Word Co-Occurrence
    ,Hands-On Exercise: Calculating Word Co-Occurrence (Optional)
    ,Hands-On Exercise: Implementing Word Co-Occurrence with a Customer WritableComparable (Optional)

    MODULE 9. Joining Data Sets in MapReduce Jobs
    Writing a Map-Side Join,Writing a Reduce-Side Join,Integrating Hadoop into the Enterprise Workflow,Integrating Hadoop into an Existing Enterprise,Loading Data from an RDBMS into HDFS by Using Sqoop,Hands-On Exercise: Importing Data with Sqoop,Managing Real-Time Data Using Flume,Accessing HDFS from Legacy Systems with FuseDFS and HttpFS

    MODULE 10. Pig
    Introduction,Installing and Running Pig,Downloading and Installing Pig,Running Pig,Grunt,Interpreting Pig Latin Scripts,HDFS Commands,Controlling PigThe Pig Data Model
    ,Data Types,Schemas,Basic Pig Latin,Input and Output,Relational Operations,User Defined Functions,Advanced Pig Latin,Advanced Relational Operations,Using Pig with Legacy Code,Integrating Pig and MapReduce,Nonlinear Data Flows,Controlling Execution,Pig Latin Preprocessor,Developing and Testing Scripts,Development Tools,Testing Your Scripts with PigUnit

    MODULE 11. HIVE
    Introduction,Getting started with HIVE,Data Types and file formats,HIVEQL – Data definition,HIVEQL – Data manipulation,HIVEQL – Queries,HIVEQL – Views,HIVEQL – Indexes
    ,Schema Design,Tuning

    MODULE 12. Hbase
    HBase architecture,HBase versions and origins,HBase vs. RDBMS,HBase Master and Region Servers,Intro to ZooKeeper,Data Modeling,Column Families and Regions,HBase Architecture Detailed,Developing with HBase,Schema Design,Schema Design Best Practices,HBase and Hive Integration,HBase Performance Optimization

    MODULE 13. Apache Flume and Chukawa
    Apache Flume introduction,Flume architecture,Flume use cases,Apache chukawa introduction,Chukwa Architecture,Chukawa use cases

    MODULE 14. Apache Oozie
    Apache oozie introduction,Installation and configuration,Oozie use cases

    MODULE 15. API (Application programming interface)
    HDFS API,PIG API,HBASE API,HIVE API

    MODULE 16. NoSQL
    Introduction,MongoDB and MapReduce,Cassendra and MapReduce

  • Spark - Shark - Hadoop Presentation at the Univ. of Colo. Denver April 23, 2013 Part 1

    1:43:24

    denverspark - Captured Live on Ustream at

    Find Part 2 at:


    Spark - Shark Data Analytics Stack on a Hadoop Cluster Presentation at the University of Colorado Denver - Tuesday April 23, 2013.


    ABSTRACT

    Data scientists need to be able to access and analyze data quickly and easily. The difference between high-value data science and good data science is increasingly about the ability to analyze larger amounts of data at faster speeds. Speed kills in data science and the ability to provide valuable, actionable insights to the client in a timely fashion can mean the difference between competitive advantage and no or little value-added.

    One flaw of Hadoop MapReduce is high latency. Considering the growing volume, variety and velocity of data, organizations and data scientists require faster analytical platforms. Put simply, speed kills and Spark gains speed through caching and optimizing the master/node communications.

    The Berkeley Data Analytics Stack (BDAS) is an open source, next-generation data analytics stack under development at the UC Berkeley AMPLab whose current components include Spark, Sharkand Mesos.

    Spark is an open source cluster computing system that makes data analytics fast. To run programs faster, Spark provides primitives for in-memory cluster computing: your job can load data into memory and query it repeatedly much quicker than with disk-based systems like Hadoop MapReduce.

    Spark is a high-speed cluster computing system compatible with Hadoop that can outperform it by up to 100 times considering its ability to perform computations in memory. It is a computation engine built on top of the Hadoop Distributed File System (HDFS) that efficiently support iterative processing (e.g., ML algorithms), and interactive queries.

    Shark is a large-scale data warehouse system that runs on top of Spark and is backward-compatible with Apache Hive, allowing users to run unmodified Hive queries on existing Hive workhouses. Shark is able to run Hive queries 100 times faster when the data fits in memory and up to 5-10 times faster when the data is stored on disk. Shark is a port of Apache Hive onto Spark that is compatible with existing Hive warehouses and queries. Shark can answer HiveQL queries up to 100 times faster than Hive without modification to the data and queries, and is also open source as part of BDAS.

    Mesos is a cluster manager that provides efficient resource isolation and sharing across distributed applications such as Hadoop, MPI, Hypertable, and Spark. As a result, Mesos allows users to easily build complex pipelines involving algorithms implemented in various frameworks.

    This presentation covers the nuts and bolts of the Spark, Shark and Mesos Data Analytics Stack on a Hadoop Cluster. We will demonstrate capabilities with a data science use-case.

    BIOS

    Michael Malak is a Data Analytics Senior Engineer at Time Warner Cable. He has been pushing computers to their limit since the 1970's. Mr. Malak earned his M.S. Math degree from George Mason University. He blogs at

    Chris Deptula is a Senior System Integration Consultant with and is responsible for data integration and implementation of Big Data systems. With over 5 years experience in data integration, business intelligence, and big data platforms, Chris has helped deploy multiple production Hadoop clusters. Prior to OpenBI, Chris was a consultant with FICO implementing marketing intelligence and fraud identification systems. Chris holds a degree in Computer and Information Technology from Purdue University. Follow Chris on Twitter @chrisdeptula.

    Michael Walker is a managing partner at Rose Business Technologies a professional technology services and systems integration firm. He leads the Data Science Professional Practice at Rose. Mr. Walker received his undergraduate degree from the University of Colorado and earned a doctorate from Syracuse University. He speaks and writes frequently about data science and is writing a book on Data Science Strategy for Business. Learn more about the Rose Data Science Professional Practice at Follow Mike on Twitter @Ironwalker76.

  • #BDAM: Apache Phoenix: OLTP in Hadoop, by James Taylor, Saleforce.com

    38:00

    EXPAND FOR MORE INFO.

    Speaker: James Taylor, Saleforce.com
    Big Data Applications Meetup, 01/27/2016
    Palo Alto, CA

    More info here:

    Link to slides:

    About the talk:

    This talk will examine how Apache Phoenix, a top level Apache project, differentiates itself from other SQL solutions in the Hadoop ecosystem. It will start with exploring some of the fundamental concepts in Phoenix that lead to dramatically better performance and explain how this enables support of features such as secondary indexing, joins, and multi-tenancy. Next, an overview of ACID transactions, a new feature available in our 4.7.0 release, will be given along with an outline of the integration we did with Tephra to enable this new capability. This will include a demo to demonstrate how Phoenix can be used seamlessly in CDAP. The talk will conclude with a discussion of some in flight work to move on top of Apache Calcite to improve query optimization, broaden our SQL support, and provide better interop with other projects such as Drill, Hive, Kylin, and Samza.

  • Big Data HadooP Tutorial by eLearningLine @ 848-200-0448

    1:2:32

    For more information about Big Data Hadoop training and please visit: || Call us: 848-200-0448 || Email us - [email protected]

    ELearningLine provides big data Hadoop training courses primarily for the people who just want to establish and boost their career in the field of Big Data using Hadoop Framework. The scope of Big Data Hadoop developer is in a growing trend in the recent days.

    Curriculum:

    1. Motivation For Hadoop
    Problems with traditional large-scale systems, Requirements for a new approach, Introducing Hadoop

    2. Hadoop: Basic Concepts
    The Hadoop Project and Hadoop Components, The Hadoop Distributed File System, Hands-On Exercise: Using HDFS, How MapReduce Works, Hands-On Exercise: Running a MapReduce Job, How a Hadoop Cluster Operates, Other Hadoop Ecosystem Projects

    3. Writing a MapReduce Program
    The MapReduce Flow, Basic MapReduce API Concepts, Writing MapReduce Drivers, Mappers and Reducers in Java, Hands-On Exercise: Writing a MapReduce Program, Differences Between the Old and New MapReduce APIs

    4. Unit Testing MapReduce Programs
    Unit Testing, The JUnit and MRUnit Testing Frameworks, Writing Unit Tests with MRUnit, Hands-On Exercise: Writing Unit Tests with the MRUnit Framework

    5. Delving Deeper into the Hadoop API
    Using the ToolRunner Class, Hands-On Exercise: Writing and Implementing a Combiner, Setting Up and Tearing Down Mappers and Reducers by Using the Configure and Close Methods, Writing Custom Partitioners for Better Load Balancing, Optional Hands-On Exercise: Writing a Partitioner, Accessing HDFS Programmatically, Using The Distributed Cache, Using the Hadoop API’s Library of Mappers, Reducers and Partitioners JSON, Ajax and PHP

    6. Development Tips and Techniques
    Strategies for Debugging MapReduce Code, Testing MapReduce Code Locally by Using LocalJobReducer, Writing and Viewing Log FilesRetrieving Job Information with Counters
    Determining the Optimal Number of Reducers for a Job
    Creating Map-Only MapReduce Jobs
    Hands-On Exercise: Using Counters and a Map-Only Job

    7. Data Input and Output
    Creating Custom Writable and WritableComparable Implementations, Saving Binary Data Using SequenceFile and Avro Data Files, Implementing Custom Input Formats and Output Formats, Issues to Consider When Using File Compression, Hands-On Exercise: Using SequenceFiles and File Compression

    8. Common MapReduce Algorithms
    Sorting and Searching Large Data Sets, Performing a Secondary Sort, Indexing Data, Hands-On Exercise: Creating an Inverted Index, Computing Term Frequency — Inverse Document Frequency, Calculating Word Co-Occurrence
    ,Hands-On Exercise: Calculating Word Co-Occurrence (Optional)
    ,Hands-On Exercise: Implementing Word Co-Occurrence with a Customer WritableComparable (Optional)

    9. Joining Data Sets in MapReduce Jobs
    Writing a Map-Side Join,Writing a Reduce-Side Join,Integrating Hadoop into the Enterprise Workflow,Integrating Hadoop into an Existing Enterprise,Loading Data from an RDBMS into HDFS by Using Sqoop,Hands-On Exercise: Importing Data with Sqoop,Managing Real-Time Data Using Flume,Accessing HDFS from Legacy Systems with FuseDFS and HttpFS

    10. Pig
    Introduction,Installing and Running Pig,Downloading and Installing Pig,Running Pig,Grunt,Interpreting Pig Latin Scripts,HDFS Commands,Controlling PigThe Pig Data Model
    ,Data Types,Schemas,Basic Pig Latin,Input and Output,Relational Operations,User Defined Functions,Advanced Pig Latin,Advanced Relational Operations,Using Pig with Legacy Code,Integrating Pig and MapReduce,Nonlinear Data Flows,Controlling Execution,Pig Latin Preprocessor,Developing and Testing Scripts,Development Tools,Testing Your Scripts with PigUnit

    11. HIVE
    Introduction,Getting started with HIVE,Data Types and file formats,HIVEQL – Data definition,HIVEQL – Data manipulation,HIVEQL – Queries,HIVEQL – Views,HIVEQL – Indexes
    ,Schema Design,Tuning

    12. Hbase
    HBase architecture,HBase versions and origins,HBase vs. RDBMS,HBase Master and Region Servers,Intro to ZooKeeper,Data Modeling,Column Families and Regions,HBase Architecture Detailed,Developing with HBase,Schema Design,Schema Design Best Practices,HBase and Hive Integration,HBase Performance Optimization

    13. Apache Flume and Chukawa
    Apache Flume introduction,Flume architecture,Flume use cases,Apache chukawa introduction,Chukwa Architecture,Chukawa use cases

    14. Apache Oozie
    Apache oozie introduction,Installation and configuration,Oozie use cases

    15. API (Application programming interface)
    HDFS API,PIG API,HBASE API,HIVE API

    16. NoSQL
    Introduction,MongoDB and MapReduce,Cassendra and MapReduce

  • Big Data Hadoop training - Single Node overview by [email protected] 848-200-0448

    1:14:50

    Big Data Hadoop training Big Data Hadoop training provided Online from USA industry expert trainers with real time project experience.
    CONTACT: 848-200-0448(or) Email - [email protected]

    ELearningLine.com provides big data Hadoop training courses primarily for the people who just want to establish and boost their career in the field of Big Data using Hadoop Framework. The scope of Big Data Hadoop developer is in a growing trend in the recent days.



    ********************************************************************

    1. Motivation For Hadoop
    Problems with traditional large-scale systems
    Requirements for a new approach
    Introducing Hadoop
    2. Hadoop: Basic Concepts
    The Hadoop Project and Hadoop Components
    The Hadoop Distributed File System
    Hands-On Exercise: Using HDFS
    How MapReduce Works
    Hands-On Exercise: Running a MapReduce Job
    How a Hadoop Cluster Operates
    Other Hadoop Ecosystem Projects
    3. Writing a MapReduce Program
    The MapReduce Flow
    Basic MapReduce API Concepts
    Writing MapReduce Drivers, Mappers and Reducers in Java
    Hands-On Exercise: Writing a MapReduce Program
    Differences Between the Old and New MapReduce APIs
    4. Unit Testing MapReduce Programs
    Unit Testing
    The JUnit and MRUnit Testing Frameworks
    Writing Unit Tests with MRUnit
    Hands-On Exercise: Writing Unit Tests with the MRUnit Framework
    5. Delving Deeper into the Hadoop API
    Using the ToolRunner Class
    Hands-On Exercise: Writing and Implementing a Combiner
    Setting Up and Tearing Down Mappers and Reducers by Using the Configure and Close Methods
    Writing Custom Partitioners for Better Load Balancing
    Optional Hands-On Exercise: Writing a Partitioner
    Accessing HDFS Programmatically
    Using The Distributed Cache
    Using the Hadoop API’s Library of Mappers, Reducers and Partitioners JSON, Ajax and PHP
    6. Development Tips and Techniques
    Strategies for Debugging MapReduce Code
    Testing MapReduce Code Locally by Using LocalJobReducer
    Writing and Viewing Log Files
    Retrieving Job Information with Counters
    Determining the Optimal Number of Reducers for a Job
    Creating Map-Only MapReduce Jobs
    Hands-On Exercise: Using Counters and a Map-Only Job
    7. Data Input and Output
    Creating Custom Writable and WritableComparable Implementations
    Saving Binary Data Using SequenceFile and Avro Data Files
    Implementing Custom Input Formats and Output Formats
    Issues to Consider When Using File Compression
    Hands-On Exercise: Using SequenceFiles and File Compression
    8. Common MapReduce Algorithms
    Sorting and Searching Large Data Sets
    Performing a Secondary Sort
    Indexing Data
    Hands-On Exercise: Creating an Inverted Index
    Computing Term Frequency — Inverse Document Frequency
    Calculating Word Co-Occurrence
    Hands-On Exercise: Calculating Word Co-Occurrence (Optional)
    Hands-On Exercise: Implementing Word Co-Occurrence with a Customer WritableComparable (Optional)
    9. Joining Data Sets in MapReduce Jobs
    Writing a Map-Side Join
    Writing a Reduce-Side Join
    Integrating Hadoop into the Enterprise Workflow
    Integrating Hadoop into an Existing Enterprise
    Loading Data from an RDBMS into HDFS by Using Sqoop
    Hands-On Exercise: Importing Data with Sqoop
    Managing Real-Time Data Using Flume
    Accessing HDFS from Legacy Systems with FuseDFS and HttpFS
    10. Pig
    Introduction
    Installing and Running Pig
    Downloading and Installing Pig
    Running Pig
    Grunt
    Interpreting Pig Latin Scripts
    HDFS Commands
    Controlling Pig
    The Pig Data Model
    Data Types
    Schemas
    Basic Pig Latin
    Input and Output
    Relational Operations
    User Defined Functions
    Advanced Pig Latin
    Advanced Relational Operations
    Using Pig with Legacy Code
    Integrating Pig and MapReduce
    Nonlinear Data Flows
    Controlling Execution
    Pig Latin Preprocessor
    Developing and Testing Scripts
    Development Tools
    Testing Your Scripts with PigUnit
    11. HIVE
    Introduction
    Getting started with HIVE
    Data Types and file formats
    HIVEQL – Data definition
    HIVEQL – Data manipulation
    HIVEQL – Queries
    HIVEQL – Views
    HIVEQL – Indexes
    Schema Design
    Tuning
    12. Hbase
    HBase architecture
    HBase versions and origins
    HBase vs. RDBMS
    HBase Master and Region Servers
    Intro to ZooKeeper
    Data Modeling
    Column Families and Regions
    HBase Architecture Detailed
    Developing with HBase
    Schema Design
    Schema Design Best Practices
    HBase and Hive Integration
    HBase Performance Optimization
    13. Apache Flume and Chukawa
    Apache Flume introduction
    Flume architecture
    Flume use cases
    Apache chukawa introduction
    Chukwa Architecture
    Chukawa use cases
    14. Apache Oozie
    Apache oozie introduction
    Installation and configuration
    Oozie use cases
    15. API (Application programming interface)
    HDFS API
    PIG API
    HBASE API
    HIVE API
    16. NoSQL
    Introduction
    MongoDB and MapReduce
    Cassendra and MapReduce

  • Big Data Hadoop Distributions training by elearningline.com @848-200-0448

    1:15:40

    Big Data Hadoop training provided Online from USA industry expert trainers with real time project experience.
    CONTACT: 848-200-0448(or) Email - [email protected]

    ELearningLine.com provides big data Hadoop training courses primarily for the people who just want to establish and boost their career in the field of Big Data using Hadoop Framework. The scope of Big Data Hadoop developer is in a growing trend in the recent days.



    ********************************************************************

    1. Motivation For Hadoop
    Problems with traditional large-scale systems
    Requirements for a new approach
    Introducing Hadoop
    2. Hadoop: Basic Concepts
    The Hadoop Project and Hadoop Components
    The Hadoop Distributed File System
    Hands-On Exercise: Using HDFS
    How MapReduce Works
    Hands-On Exercise: Running a MapReduce Job
    How a Hadoop Cluster Operates
    Other Hadoop Ecosystem Projects
    3. Writing a MapReduce Program
    The MapReduce Flow
    Basic MapReduce API Concepts
    Writing MapReduce Drivers, Mappers and Reducers in Java
    Hands-On Exercise: Writing a MapReduce Program
    Differences Between the Old and New MapReduce APIs
    4. Unit Testing MapReduce Programs
    Unit Testing
    The JUnit and MRUnit Testing Frameworks
    Writing Unit Tests with MRUnit
    Hands-On Exercise: Writing Unit Tests with the MRUnit Framework
    5. Delving Deeper into the Hadoop API
    Using the ToolRunner Class
    Hands-On Exercise: Writing and Implementing a Combiner
    Setting Up and Tearing Down Mappers and Reducers by Using the Configure and Close Methods
    Writing Custom Partitioners for Better Load Balancing
    Optional Hands-On Exercise: Writing a Partitioner
    Accessing HDFS Programmatically
    Using The Distributed Cache
    Using the Hadoop API’s Library of Mappers, Reducers and Partitioners JSON, Ajax and PHP
    6. Development Tips and Techniques
    Strategies for Debugging MapReduce Code
    Testing MapReduce Code Locally by Using LocalJobReducer
    Writing and Viewing Log Files
    Retrieving Job Information with Counters
    Determining the Optimal Number of Reducers for a Job
    Creating Map-Only MapReduce Jobs
    Hands-On Exercise: Using Counters and a Map-Only Job
    7. Data Input and Output
    Creating Custom Writable and WritableComparable Implementations
    Saving Binary Data Using SequenceFile and Avro Data Files
    Implementing Custom Input Formats and Output Formats
    Issues to Consider When Using File Compression
    Hands-On Exercise: Using SequenceFiles and File Compression
    8. Common MapReduce Algorithms
    Sorting and Searching Large Data Sets
    Performing a Secondary Sort
    Indexing Data
    Hands-On Exercise: Creating an Inverted Index
    Computing Term Frequency — Inverse Document Frequency
    Calculating Word Co-Occurrence
    Hands-On Exercise: Calculating Word Co-Occurrence (Optional)
    Hands-On Exercise: Implementing Word Co-Occurrence with a Customer WritableComparable (Optional)
    9. Joining Data Sets in MapReduce Jobs
    Writing a Map-Side Join
    Writing a Reduce-Side Join
    Integrating Hadoop into the Enterprise Workflow
    Integrating Hadoop into an Existing Enterprise
    Loading Data from an RDBMS into HDFS by Using Sqoop
    Hands-On Exercise: Importing Data with Sqoop
    Managing Real-Time Data Using Flume
    Accessing HDFS from Legacy Systems with FuseDFS and HttpFS
    10. Pig
    Introduction
    Installing and Running Pig
    Downloading and Installing Pig
    Running Pig
    Grunt
    Interpreting Pig Latin Scripts
    HDFS Commands
    Controlling Pig
    The Pig Data Model
    Data Types
    Schemas
    Basic Pig Latin
    Input and Output
    Relational Operations
    User Defined Functions
    Advanced Pig Latin
    Advanced Relational Operations
    Using Pig with Legacy Code
    Integrating Pig and MapReduce
    Nonlinear Data Flows
    Controlling Execution
    Pig Latin Preprocessor
    Developing and Testing Scripts
    Development Tools
    Testing Your Scripts with PigUnit
    11. HIVE
    Introduction
    Getting started with HIVE
    Data Types and file formats
    HIVEQL – Data definition
    HIVEQL – Data manipulation
    HIVEQL – Queries
    HIVEQL – Views
    HIVEQL – Indexes
    Schema Design
    Tuning
    12. Hbase
    HBase architecture
    HBase versions and origins
    HBase vs. RDBMS
    HBase Master and Region Servers
    Intro to ZooKeeper
    Data Modeling
    Column Families and Regions
    HBase Architecture Detailed
    Developing with HBase
    Schema Design
    Schema Design Best Practices
    HBase and Hive Integration
    HBase Performance Optimization
    13. Apache Flume and Chukawa
    Apache Flume introduction
    Flume architecture
    Flume use cases
    Apache chukawa introduction
    Chukwa Architecture
    Chukawa use cases
    14. Apache Oozie
    Apache oozie introduction
    Installation and configuration
    Oozie use cases
    15. API (Application programming interface)
    HDFS API
    PIG API
    HBASE API
    HIVE API
    16. NoSQL
    Introduction
    MongoDB and MapReduce
    Cassendra and MapReduce

  • Spark - Shark - Hadoop Presentation at the Univ. of Colo. Denver April 23, 2013 Part 2

    12:20

    Big Data Week presentations - Captured Live on Ustream at

    Find Part 1 at:


    Spark - Shark Data Analytics Stack on a Hadoop Cluster Presentation at the University of Colorado Denver - Tuesday April 23, 2013.


    ABSTRACT

    Data scientists need to be able to access and analyze data quickly and easily. The difference between high-value data science and good data science is increasingly about the ability to analyze larger amounts of data at faster speeds. Speed kills in data science and the ability to provide valuable, actionable insights to the client in a timely fashion can mean the difference between competitive advantage and no or little value-added.

    One flaw of Hadoop MapReduce is high latency. Considering the growing volume, variety and velocity of data, organizations and data scientists require faster analytical platforms. Put simply, speed kills and Spark gains speed through caching and optimizing the master/node communications.

    The Berkeley Data Analytics Stack (BDAS) is an open source, next-generation data analytics stack under development at the UC Berkeley AMPLab whose current components include Spark, Sharkand Mesos.

    Spark is an open source cluster computing system that makes data analytics fast. To run programs faster, Spark provides primitives for in-memory cluster computing: your job can load data into memory and query it repeatedly much quicker than with disk-based systems like Hadoop MapReduce.

    Spark is a high-speed cluster computing system compatible with Hadoop that can outperform it by up to 100 times considering its ability to perform computations in memory. It is a computation engine built on top of the Hadoop Distributed File System (HDFS) that efficiently support iterative processing (e.g., ML algorithms), and interactive queries.

    Shark is a large-scale data warehouse system that runs on top of Spark and is backward-compatible with Apache Hive, allowing users to run unmodified Hive queries on existing Hive workhouses. Shark is able to run Hive queries 100 times faster when the data fits in memory and up to 5-10 times faster when the data is stored on disk. Shark is a port of Apache Hive onto Spark that is compatible with existing Hive warehouses and queries. Shark can answer HiveQL queries up to 100 times faster than Hive without modification to the data and queries, and is also open source as part of BDAS.

    Mesos is a cluster manager that provides efficient resource isolation and sharing across distributed applications such as Hadoop, MPI, Hypertable, and Spark. As a result, Mesos allows users to easily build complex pipelines involving algorithms implemented in various frameworks.

    This presentation covers the nuts and bolts of the Spark, Shark and Mesos Data Analytics Stack on a Hadoop Cluster. We will demonstrate capabilities with a data science use-case.

    BIOS

    Michael Malak is a Data Analytics Senior Engineer at Time Warner Cable. He has been pushing computers to their limit since the 1970's. Mr. Malak earned his M.S. Math degree from George Mason University. He blogs at

    Chris Deptula is a Senior System Integration Consultant with and is responsible for data integration and implementation of Big Data systems. With over 5 years experience in data integration, business intelligence, and big data platforms, Chris has helped deploy multiple production Hadoop clusters. Prior to OpenBI, Chris was a consultant with FICO implementing marketing intelligence and fraud identification systems. Chris holds a degree in Computer and Information Technology from Purdue University. Follow Chris on Twitter @chrisdeptula.

    Michael Walker is a managing partner at Rose Business Technologies a professional technology services and systems integration firm. He leads the Data Science Professional Practice at Rose. Mr. Walker received his undergraduate degree from the University of Colorado and earned a doctorate from Syracuse University. He speaks and writes frequently about data science and is writing a book on Data Science Strategy for Business. Learn more about the Rose Data Science Professional Practice at Follow Mike on Twitter @Ironwalker76.

  • HUG Meetup Oct 2016: Architecture of an Open Source RDBMS powered by HBase and Spark

    35:41

    Splice Machine is an open-source database that combines the benefits of modern lambda architectures with the full expressiveness of ANSI-SQL. Like lambda architectures, it employs separate compute engines for different workloads - some call this an HTAP database (Hybrid Transactional and Analytical Platform). This talk describes the architecture and implementation of Splice Machine V2.0. The system is powered by a sharded key-value store for fast short reads and writes, and short range scans (Apache HBase) and an in-memory, cluster data flow engine for analytics (Apache Spark). It differs from most other clustered SQL systems such as Impala, SparkSQL, and Hive because it combines analytical processing with a distributed Multi-Value Concurrency Method that provides fine-grained concurrency which is required to power real-time applications. This talk will highlight the Splice Machine storage representation, transaction engine, cost-based optimizer, and present the detailed execution of operational queries on HBase, and the detailed execution of analytical queries on Spark. We will compare and contrast how Splice Machine executes queries with other HTAP systems such as Apache Phoenix and Apache Trafodian. We will end with some roadmap items under development involving new row-based and column-based storage encodings. 

    Speakers:
    Monte Zweben, is a technology industry veteran. Monte’s early career was spent with the NASA Ames Research Center as the Deputy Chief of the Artificial Intelligence Branch, where he won the prestigious Space Act Award for his work on the Space Shuttle program. He then founded and was the Chairman and CEO of Red Pepper Software, a leading supply chain optimization company, which merged in 1996 with PeopleSoft, where he was VP and General Manager, Manufacturing Business Unit. In 1998, he was the founder and CEO of Blue Martini Software – the leader in e-commerce and multi-channel systems for retailers. Blue Martini went public on NASDAQ in one of the most successful IPOs of 2000, and is now part of JDA. Following Blue Martini, he was the chairman of SeeSaw Networks, a digital, place-based media company. Monte is also the co-author of Intelligent Scheduling and has published articles in the Harvard Business Review and various computer science journals and conference proceedings. He currently serves on the Board of Directors of Rocket Fuel Inc. as well as the Dean’s Advisory Board for Carnegie-Mellon’s School of Computer Science.

  • HUG Meetup January 2012: HCatalog Overview

    8:33

    Note: we have been experiencing technical difficulties with the audio and video, so we apologize for the recordings quality.

    HCatalog is a table abstraction and a storage abstraction system that makes it easy for multiple tools to interact with the same underlying data. A common buzzword in the NoSQL world today is that of polyglot persistence. Basically, what that comes down to is that you pick the right tool for the job. In the hadoop ecosystem, you have many tools that might be used for data processing - you might use pig or hive, or your own custom mapreduce program, or that shiny new GUI-based tool that's just come out. And which one to use might depend on the user, or on the type of query you're interested in, or the type of job we want to run. From another perspective, you might want to store your data in columnar storage for efficient storage and retrieval for particular query types, or in text so that users can write data producers in scripting languages like perl or python, or you may want to hook up that hbase table as a data source. As a end-user, I want to use whatever data processing tool is available to me. As a data designer, I want to optimize how data is stored. As a cluster manager / data architect, I want the ability to share pieces of information across the board, and move data back and forth fluidly. HCatalog's hopes and promises are the realization of all of the above.

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