Hadoop and the Relational Database: The Best of Both Worlds
- 1. Grab some coffee and enjoy the pre-show banter before the top of the hour!
- 2. Hadoop and the Relational Database: The Best of Both Worlds The Briefing Room
- 3. Twitter Tag: #briefr The Briefing Room Welcome Host: Eric Kavanagh [email protected] @eric_kavanagh
- 4. ! Reveal the essential characteristics of enterprise software, good and bad ! Provide a forum for detailed analysis of today’s innovative technologies ! Give vendors a chance to explain their product to savvy analysts ! Allow audience members to pose serious questions... and get answers! Twitter Tag: #briefr The Briefing Room Mission
- 5. This Month: BIG DATA ECOSYSTEM September: INTEGRATION & DATA FLOW October: ANALYTIC PLATFORMS Twitter Tag: #briefr The Briefing Room Topics 2014 Editorial Calendar at www.insideanalysis.com/webcasts/the-briefing-room
- 6. Twitter Tag: #briefr The Briefing Room Executive Summary Scale out is the new Agile Business needs constant flexibility No time for down time Grow as quickly as you can sell
- 7. Twitter Tag: #briefr The Briefing Room Analyst: Robin Bloor Robin Bloor is Chief Analyst at The Bloor Group [email protected] @robinbloor
- 8. Twitter Tag: #briefr The Briefing Room Splice Machine ! Splice Machine is a SQL-on-Hadoop database ! The product is ACID-compliant and can power both OLAP and OLTP workloads ! Splice Machine is built on Java-based Apache Derby and Hbase/Hadoop
- 9. Guests: John Leach & Rich Reimer John Leach, Co-Founder and Chief Technology Officer With over 15 years of software experience under his belt, John’s expertise in analytics and BI drives his role as Chief Technology Officer. Prior to Splice Machine, John founded Incite Retail in June 2008 and led the company’s strategy and development efforts. Prior to Incite Retail, he ran the business intelligence practice at Blue Martini Software and built strategic partnerships with integration partners. His focus at Blue Martini was helping clients incorporate decision support knowledge into their current business processes utilizing advanced algorithms and machine learning. Rich Reimer, VP of Marketing and Product Management Rich has over 15 years of sales, marketing and management experience in high-tech Treasure Isle studio head, where he used petabytes of data from millions of daily users to optimize the business in real-time. Prior to Zynga, he was the COO and co-founder of a social media platform named Grouply. Before founding Grouply, Rich held executive positions at Siebel Systems, Blue Martini Software and Oracle Corporation as well as sales and marketing positions at General Electric and Bell Atlantic. Twitter Tag: #briefr companies. Before joining Splice Machine, Rich worked at Zynga as the The Briefing Room
- 10. Affordable Scale-‐Out August 5, 2014
- 11. 11 Data Doubling Every 2 Years… Driven by web, social, mobile, and Internet of Things Source: 2013 IBM Briefing Book
- 12. 12 TradiBonal RDBMSs Overwhelmed… Scale-‐up becoming cost-‐prohibi=ve Oracle is too darn expensive! My DB is hiLng the wall Users keep geLng those spinning beach balls We have to throw data away Our reports take forever
- 13. 13 Case Study: Harte-‐Hanks Overview ! Digital markeBng services provider ! Real-‐Bme campaign management ! Complex OLTP and OLAP environment Challenges ! Oracle RAC too expensive to scale ! Queries too slow – even up to ½ hour ! GeLng worse – expect 30-‐50% data growth ! Looked for 9 months for a cost-‐effecBve soluBon SoluBon Diagram IniBal Results 10-‐20x price/perf with no applicaBon, BI or ETL rewrites ¼ cost with commodity scale out 3-‐7x faster through parallelized queries Cross-Channel Campaigns Real-Time Personalization Real-Time Actions
- 14. 14 Scale-‐Out: The Future of Databases Drama=c improvement in price/performance Scale Up (Increase server size) Scale Out (More small servers) $ vs. $ $ $ $ $
- 15. 15 Who are We? THE ONLY HADOOP RDBMS Replace your old RDBMS with a scale-‐out SQL database ! Affordable, Scale-‐Out ! ACID TransacBons ! No ApplicaBon Rewrites 10x Beier Price/Perf
- 16. 16 Customer Performance Benchmarks Typically 10x price/performance improvement 30x 3-‐7x 10-‐20x 10x 20x 10-‐15x 7x 5x SPEED VS. PRICE/PERFORMANCE
- 17. Use Cases § Digital MarkeBng § Campaign management § Unified Customer Profile § Real-‐Bme personalizaBon § Data Lake § OperaBonal reporBng and analyBcs § OperaBonal Data Stores § Fraud DetecBon § Personalized Medicine § Internet of Things § Network monitoring § Cyber-‐threat security § Wearables and sensors 17
- 18. Seasoned Team 18 Successful Serial Entrepreneurs Enterprise So?ware Experience Database & Big Data Experience Big Data Research & Community Leadership Hadoop User Group
- 19. What People are Saying… 19 Recognized as a key innovator in databases Scaling out on Splice Machine presented some major benefits over Oracle ...automaBc balancing between clusters...avoiding the costly licensing issues. Quotes Awards An alternaKve to today’s RDBMSes, Splice Machine effecBvely combines tradiBonal relaBonal database technology with the scale-‐out capabiliBes of Hadoop. The unique claim of … Splice Machine is that it can run transacKonal applicaKons as well as support analyBcs on top of Hadoop.
- 20. 20 Proven Building Blocks: Hadoop and Derby APACHE DERBY § ANSI SQL-‐99 RDBMS § Java-‐based § ODBC/JDBC Compliant APACHE HBASE/HDFS § Auto-‐sharding § Real-‐Bme updates § Fault-‐tolerance § Scalability to 100s of PBs § Data replicaBon
- 21. 21 HBase: Proven Scale-‐Out § Auto-‐sharding § Scales with commodity hardware § Cost-‐effecBve from GBs to PBs § High availability thru failover and replicaBon § LSM-‐trees
- 22. 22 Distributed, Parallelized Query ExecuBon ! Parallelized computaBon across cluster ! Moves computaBon to the data ! UBlizes HBase co-‐processors ! No MapReduce
- 23. ANSI SQL-‐99 Coverage 23 § Data types – e.g., INTEGER, REAL, CHARACTER, DATE, BOOLEAN, BIGINT § DDL – e.g., CREATE TABLE, CREATE SCHEMA, ALTER TABLE, DELETE, UPDATE § Predicates – e.g., IN, BETWEEN, LIKE, EXISTS § DML – e.g., INSERT, DELETE, UPDATE, SELECT § Query specificaKon – e.g., SELECT DISTINCT, GROUP BY, HAVING § SET funcKons – e.g., UNION, ABS, MOD, ALL, CHECK § AggregaKon funcKons – e.g., AVG, MAX, COUNT § String funcKons – e.g., SUBSTRING, concatenaBon, UPPER, LOWER, POSITION, TRIM, LENGTH § CondiKonal funcKons – e.g., CASE, searched CASE § Privileges – e.g., privileges for SELECT, DELETE, INSERT, EXECUTE § Cursors – e.g., updatable, read-‐only, posiBoned DELETE/UPDATE § Joins – e.g., INNER JOIN, LEFT OUTER JOIN § TransacKons – e.g., COMMIT, ROLLBACK, READ COMMITTED, REPEATABLE READ, READ UNCOMMITTED, Snapshot IsolaBon § Sub-‐queries § Triggers § User-‐defined funcKons (UDFs) § Views – including grouped views
- 24. 24 Lockless, ACID transacBons State-‐of-‐the-‐Art Snapshot Isola=on Transaction C ! Adds mulB-‐row, mulB-‐table transacBons to HBase with rollback ! Fast, lockless, high concurrency ! ZooKeeper coordinaBon ! Extends research from Google Percolator, Yahoo Labs, U of Waterloo Transaction A Transaction B Ts Tc
- 25. 25 BI and SQL tool support via ODBC No applica=on rewrites needed
- 26. 26 Who are We? THE ONLY HADOOP RDBMS Replace your old RDBMS with a scale-‐out SQL database ! Affordable, Scale-‐Out ! ACID TransacBons ! No ApplicaBon Rewrites 10x Beier Price/Perf
- 27. Thank You!
- 28. Twitter Tag: #briefr The Briefing Room Perceptions & Questions Analyst: Robin Bloor
- 29. Hadoop as a Data Refinery? Robin Bloor, PhD
- 30. Data Flow – A Set of Principles u The data layer is one logical collection of data, both external and internal u The data flows, from ingest through a refining process to a point of application u It is best if data doesn’t flow much u “Vanilla Hadoop” is a viable catching & refining vehicle u Beyond that a database is required to manage workloads
- 32. Data Refining
- 33. The Data Engines STREAMING DATA OLTP LARGE QUERY LARGE ANALYTICAL QUERY SQL, JSON, SPARQL QUERIES
- 34. u How does Splice Machine organize its data? u Is this an OLTP database or a BI database? Or can it be both at the same time? u What do you see as the sweet spot for this database: • In respect of Big Data? • In respect of business applications?
- 35. u Is Splice Machine also suited for analytical applications? u Do you also find yourselves competing with NoSQL products? u In respect of scale, what is your largest implementation by data volume and by transaction rate?
- 36. Twitter Tag: #briefr The Briefing Room
- 37. This Month: BIG DATA ECOSYSTEM September: INTEGRATION & DATA FLOW October: ANALYTIC PLATFORMS www.insideanalysis.com/webcasts/the-briefing-room Twitter Tag: #briefr The Briefing Room Upcoming Topics 2014 Editorial Calendar at www.insideanalysis.com
- 38. Twitter Tag: #briefr THANK YOU for your ATTENTION! Opening slide image courtesy of Wikimedia Commons The Briefing Room