Testing Big Data: Automated ETL Testing of Hadoop
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Testing Big Data: Automated ETL Testing of Hadoop



Learn why testing your enterprise's data is pivotal for success with Big Data and Hadoop. See how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of ...

Learn why testing your enterprise's data is pivotal for success with Big Data and Hadoop. See how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool.



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  • Largest known cluster is 4500 nodes
  • Designing and maintaining the ETL process is often considered one of the most difficult and resource-intensive portions of a data warehouse project. Many data warehousing projects use ETL tools to manage this process. Other data warehouse builders create their own ETL tools and processes, either inside or outside the database.Besides the support of extraction, transformation, and loading, there are some other tasks that are important for a successful ETL implementation as part of the daily operations of the data warehouse and its support for further enhancements.
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Testing Big Data: Automated ETL Testing of Hadoop Testing Big Data: Automated ETL Testing of Hadoop Presentation Transcript

  • Webinar Testing Big Data: Automated ETL Testing of Hadoop Laura Poggi Marketing Manager RTTS Bill Hayduk CEO/President RTTS Jeff Bocarsly, Ph.D. Chief Architect RTTS built by
  • Today’s Agenda • About Big Data and Hadoop • Data Warehouse refresher AGENDA Topic: Testing Big Data: Automated ETL Testing of Hadoop • Hadoop and DWH Use Case Host: RTTS Date: Thursday, January 30, 2014 Time: 1:00 pm, Eastern Standard Time (New York, GMT-05:00) • How to test Big Data Session number:630 771 732 • Demo of QuerySurge & Hadoop built by
  • FACTS Founded: 1996 About Primary Focus: consulting services, software Locations: New York, Atlanta, Philly, Phoenix Geographic region: North America Customer profile: Fortune 1000, > 600 clients Software: RTTS is the leading provider of software quality for critical business systems
  • Facebook handles 300 million photos a day and about 105 terabytes of data every 30 minutes. - TechCrunch The big data market will grow from $3.2 billion in 2010 to $32.4 billion in 2017. - Research Firm IDC 65% of…advanced analytics will have Hadoop embedded (in them) by 2015. -Gartner built by
  • about Big Data Big data – defined as too much volume, velocity and variety to work on normal database architectures. Size Defined as 5 petabytes or more 1 petabyte = 1,000 terabytes 1,000 terabytes = 1,000,000 gigabytes 1,000,000 gigabytes = 1,000,000,000 megabytes built by
  • ? What is Hadoop is an open source project that develops software for scalable, distributed computing. • • is a of large data sets across clusters of computers using simple programming models. easily deals with complexities of high of data from single servers to 1,000’s of machines, each offering local computation and storage. • detects and at the application layer built by
  • Key Attributes of Hadoop • Redundant and reliable • Extremely powerful • Easy to program distributed apps • Runs on commodity hardware built by
  • Basic Hadoop Architecture MapReduce – processing part that manages the programming jobs. (a.k.a. Task Tracker) HDFS (Hadoop Distributed File System) – stores data on the machines. (a.k.a. Data Node) MapReduce (Task Tracker) HDFS (Data Node) machine built by
  • Basic Hadoop Architecture (continued) Cluster Add more machines for scaling – from 1 to 100 to 1,000 Job Tracker accepts jobs, assigns tasks, identifies failed machines Task Task Task Task Task Task Task Task Task Task Task Task Tracker Tracker Tracker Tracker Tracker Tracker Tracker Tracker Tracker Tracker Tracker Tracker Data Data Data Data Data Data Data Data Data Data Data Data Node Node Node Node Node Node Node Node Node Node Node Node Name Node Name Node Coordination for HDFS. Inserts and extraction are communicated through the Name Node. built by
  • Apache Hive Apache Hive - a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. Hive provides a mechanism to query the data using a SQL-like language called HiveQL that interacts with the HDFS files MapReduce • • • • • (Task Tracker) create insert update delete select HiveQL HiveQL HiveQL HiveQL HiveQL HDFS (Data Node) built by
  • Data Warehouse Review built by
  • about Data Warehouses… Data Warehouse • typically a relational database that is designed for query and analysis rather than for transaction processing • a place where historical data is stored for archival, analysis and security purposes. • contains either raw data or formatted data • combines data from multiple sources • • • • • • • • • Sales salaries operational data human resource data inventory data web logs Social networks Internet text and docs other built by
  • Data Warehousing: the ETL process ETL = Extract, Transform, Load Why ETL? Need to load the data warehouse regularly (daily/weekly) so that it can serve its purpose of facilitating business analysis. Extract - data from one or more OLTP systems and copied into the warehouse Transform – removing inconsistencies, assemble to a common format, adding missing fields, summarizing detailed data and deriving new fields to store calculated data. Load – map the data and load it into the DWH built by
  • Data Warehouse – the ETL process Source Data Legacy DB ETL Process Target DWH Extract CRM/ERP DB Finance DB Transform Load built by
  • Data Warehouse & Hadoop: A Use Case DWH Hadoop built by
  • DWH & Hadoop: A Use Case USE CASE*** Use Hadoop as a landing zone for big data & raw data 1) bring all raw, big data into Hadoop 2) perform some pre-processing of this data 3) determine which data goes to EDWH 4) Extract, transform and load (ETL) pertinent data into EDHW ***Source: Vijay Ramaiah, IBM product manager, datanami magazine, June 10, 2013 built by
  • DWH & Hadoop: A Use Case Use case data flow Source Data Source ETL Process Target DWH ETL built by
  • Testing Big Data built by
  • Testing Big Data: Entry Points Recommended functional test strategy: Test every entry point in the system (feeds, databases, internal messaging, front-end transactions). The goal: provide rapid localization of data issues between points test entry point test entry point Source Data Source Hadoop ETL Process Target DWH B I ETL built by
  • Testing Big Data: 3 Big Issues - we need to verify more data and to do it faster - we need to automate the testing effort - We need to be able to test across different platforms We need a testing tool! built by
  • About QuerySurge built by 21
  • What is QuerySurge? QuerySurge is the premier test tool built to automate Data Warehouse testing and the ETL Testing Process built by
  • What does QuerySurge ™do? QuerySurge finds bad data • Most firms test < 1% of their data • BI apps sit on top of DWHs that have at best, untested data & at worst, bad data • CEOs, CFOs, CTOs, executives rely on BI apps to make strategic decisions • Bad data will cause execs to make decisions that will cost them $millions • QuerySurge tests up to 100% of your data quickly & finds bad data built by
  • QuerySurge Roles & Uses Testers - functional testing - regression testing ETL Developers - unit testing Data Analysts - review, analyze data - verify mappings and failures. Operations teams - monitoring built by
  • QuerySurge™ Architecture Sources Target built by
  • QuerySurge™ Modules Design Library  Create Query Pairs (source & target queries) Scheduling  Build groups of Query Pairs  Schedule Test Runs built by 26
  • QuerySurge™ Modules Run Dashboard  View real-time execution  Analyze real-time results Deep-Dive Reporting  Examine and automatically email test results built by
  • the QuerySurge solution… verifies more data  verifies upwards of 100% of all data quickly automates the testing effort the kickoff, the tests, the comparison, emailing the results tests across different platforms any JDBC-compliant db, DWH, DMart, flat file, XML, Hadoop speeds up testing up to 1,000 times faster than manual testing built by
  • QuerySurge Value-Add QuerySurge provides value by either: in testing data coverage from < 1% to upwards of 100% in testing time by as much as 1,000 x combination of testing time in test coverage while in built by 29
  • Return on Investment (ROI)  redeployment of head count because of an increase in coverage  a savings over manual testing (minus queries, manual compares, other)  an increase in better data due to shorter / more thorough testing cycle, possibly saving $ millions by preventing key decisions made on bad data. built by 30
  • Demonstration Jeff Bocarsly, Ph.D. Chief Architect RTTS Ensuring Data Warehouse Quality