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From lots of reports (with some data Analysis) to Massive Data Analysis (With some Reporting)
The document presents a talk by Mark Rittman, an expert in Oracle business intelligence and data warehousing, discussing the evolution of BI tools from traditional reporting to modern self-service analytics powered by Hadoop. It outlines the significant changes in BI architecture, including the transition to cloud and NoSQL databases, while emphasizing the importance of flexible and scalable data processing frameworks. Rittman also highlights the emergence of new analytics platforms that integrate advanced techniques like machine learning and real-time data access.
From lots of reports (with some data Analysis) to Massive Data Analysis (With some Reporting)
- 1. EVENT SPEAKER DANISH BIMEETUP, SEP’ 2016 FROM LOTS OF REPORTS (WITH SOME DATA ANALYSIS) TO MASSIVE DATA ANALYSIS (WITH SOME REPORTING) MARK RITTMAN, ORACLE ACE DIRECTOR
- 2. info@rittmanmead.com www.rittmanmead.com @rittmanmead2 •Mark Rittman, Co-Founder of Rittman Mead ‣Oracle ACE Director, specialising in Oracle BI&DW ‣14 Years Experience with Oracle Technology ‣Regular columnist for Oracle Magazine •Author of two Oracle Press Oracle BI books ‣Oracle Business Intelligence Developers Guide ‣Oracle Exalytics Revealed ‣Writer for Rittman Mead Blog : http://www.rittmanmead.com/blog •Email : mark.rittman@rittmanmead.com •Twitter : @markrittman About the Speaker
- 3. info@rittmanmead.com www.rittmanmead.com @rittmanmead3 •Started back in 1996 on a bank Oracle DW project •Our tools were Oracle 7.3.4, SQL*Plus, PL/SQL and shell scripts •Went on to use Oracle Developer/2000 and Designer/2000 •Our initial users queried the DW using SQL*Plus •And later on, we rolled-out Discoverer/2000 to everyone else •And life was fun… 20 Years in Oracle BI and Data Warehousing
- 4. info@rittmanmead.com www.rittmanmead.com @rittmanmead4 •Data warehouses provided a unified view of the business ‣Single place to store key data and metrics ‣Joined-up view of the business ‣Aggregates and conformed dimensions ‣ETL routines to load, cleanse and conform data •BI tools for simple, guided access to information ‣Tabular data access using SQL-generating tools ‣Drill paths, hierarchies, facts, attributes ‣Fast access to pre-computed aggregates ‣Packaged BI for fast-start ERP analytics Data Warehouses and Enterprise BI Tools Oracle MongoDB Oracle Sybase IBM DB/2 MS SQL MS SQL Server Core ERP Platform Retail Banking Call Center E-Commerce CRM Business Intelligence Tools Data Warehouse Access & Performance Layer ODS / Foundation Layer 4
- 5. info@rittmanmead.com www.rittmanmead.com @rittmanmead5 •Examples were Crystal Reports, Oracle Reports, Cognos Impromptu, Business Objects •Report written against carefully-curated BI dataset, or directly connecting to ERP/CRM •Adding data from external sources, or other RDBMSs, was difficult and involved IT resources •Report-writing was a skilled job •High ongoing cost for maintenance and changes •Little scope for analysis, predictive modeling •Often user frustration and pace of delivery Reporting Back Then… 5
- 6. info@rittmanmead.com www.rittmanmead.com @rittmanmead6 •For example Oracle OBIEE, SAP Business Objects, IBM Cognos •Full-featured, IT-orientated enterprise BI platforms •Metadata layers, integrated security, web delivery •Pre-build ERP metadata layers, dashboards + reports •Federated queries across multiple sources •Single version of the truth across the enterprise •Mobile, web dashboards, alerts, published reports •Integration with SOA and web services Then Came Enterprise BI Tools 6
- 7. info@rittmanmead.com www.rittmanmead.com @rittmanmead TraditionalThree-Layer Relational Data Warehouses Staging Foundation / ODS Performance / Dimensional ETL ETL BI Tool (OBIEE) with metadata layer OLAP / In-Memory Tool with data load into own database Direct Read Data Load Traditional structured data sources Data Load Data Load Data Load Traditional Relational Data Warehouse •Three-layer architecture - staging, foundation and access/performance •All three layers stored in a relational database (Oracle) •ETL used to move data from layer-to-layer
- 8. And All WasGood…
- 9. (a big BIproject)
- 11. Lots of reports (with some data analysis)
- 12. Meanwhile… The world gotdigitised
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- 15. and users gotimpatient…
- 17. Reporting and Dashboards… becameself-service data discovery
- 18. Advanced analytics foreveryone
- 19. Cloud and SaaShave won
- 21.
- 22. info@rittmanmead.com www.rittmanmead.com @rittmanmead TheGartner BI & Analytics Magic Quadrant 2016
- 25. info@rittmanmead.com www.rittmanmead.com @rittmanmead29 Analytic Workflow Component Traditional BI Platform Modern BI Platform Data source Upfront dimensional modeling required (IT-built star schemas) Upfront modeling not required (flat files/ flat tables) Data ingestion and preparation IT-produced IT-enabled Content authoring Primarily IT staff, but also some power users Business users Analysis Predefined, ad hoc reporting, based on predefined model Free-form exploration Insight delivery Distribution and notifications via scheduled reports or portal Sharing and collaboration, storytelling, open APIs Gartner’s View of A “Modern BI Platform” in 2016
- 26. 2007 - 2015 Diedof ingratitude by business users Just when we got the infrastructure right Doesn’t anyone appreciate a single version of the truth? Don’t say we didn’t warn you No you can’t just export it to Excel Watch out OLAP you’re next
- 27.
- 28. info@rittmanmead.com www.rittmanmead.com @rittmanmead32 •Data now landed in Hadoop clusters, NoSQL databases and Cloud Storage •Flexible data storage platform with cheap storage, flexible schema support + compute •Data lands in the data lake or reservoir in raw form, then minimally processed •Data then accessed directly by “data scientists”, or processed further into DW Meet the New Data Warehouse : The “Data Reservoir” Data Transfer Data Access Data Factory Data Reservoir Business Intelligence Tools Hadoop Platform File Based Integration Stream Based Integration Data streams Discovery & Development Labs Safe & secure Discovery and Development environment Data sets and samples Models and programs Marketing / Sales Applications Models Machine Learning Segments Operational Data Transactions Customer Master ata Unstructured Data Voice + Chat Transcripts ETL Based Integration Raw Customer Data Data stored in the original format (usually files) such as SS7, ASN.1, JSON etc. Mapped Customer Data Data sets produced by mapping and transforming raw data
- 29. Hadoop is thenew Data Warehouse
- 30. info@rittmanmead.com www.rittmanmead.com @rittmanmead Hadoop: The Default Platform Today for Analytics •Enterprise High-End RDBMSs such as Oracle can scale into the petabytes, using clustering ‣Sharded databases (e.g. Netezza) can scale further but with complexity / single workload trade-offs •Hadoop was designed from outside for massive horizontal scalability - using cheap hardware •Anticipates hardware failure and makes multiple copies of data as protection •More nodes you add, more stable it becomes •And at a fraction of the cost of traditional RDBMS platforms
- 31. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Datafrom new-world applications is not like historic data •Typically comes in non-tabular form •JSON, log files, key/value pairs •Users often want it speculatively •Haven’t thought it through •Schema can evolve •Or maybe there isn’t one •But the end-users want it now •Not when you’re ready 35 But Why Hadoop? Reason #1 - Flexible Storage Big Data Management Platform Discovery & Development Labs Safe & secure Discovery and Development environment Data sets and samples Models and programs Single Customer View Enriched Customer Profile Correlating Modeling Machine Learning Scoring Schema-on Read Analysis
- 32. info@rittmanmead.com www.rittmanmead.com @rittmanmead ButWhy Hadoop? Reason #2 - Massive Scalability •Enterprise High-End RDBMSs such as Oracle can scale ‣Clustering for single-instance DBs can scale to >PB ‣Exadata scales further by offloading queries to storage ‣Sharded databases (e.g. Netezza) can scale further ‣But cost (and complexity) become limiting factors ‣Typically $1m/node is not uncommon
- 33. info@rittmanmead.com www.rittmanmead.com @rittmanmead ButWhy Hadoop? Reason #2 - Massive Scalability
- 34. info@rittmanmead.com www.rittmanmead.com @rittmanmead ButWhy Hadoop? Reason #2 - Massive Scalability •Hadoop’s main design goal was to enable virtually-limitless horizontal scalability •Rather than a small number of large, powerful servers, it spreads processing over large numbers of small, cheap, redundant servers •Processes the data where it’s stored, avoiding I/O bottlenecks •The more nodes you add, the more stable it becomes! •At an affordable cost - this is key •$50k/node vs. $1m/node •And … the Hadoop platform is a better fit for new types of processing and analysis
- 35. info@rittmanmead.com www.rittmanmead.com @rittmanmead Big Data Platform - All Running Natively Under Hadoop YARN (Cluster Resource Management) Batch (MapReduce) HDFS (Cluster Filesystem holding raw data) Interactive (Impala, Drill, Tez, Presto) Streaming + In-Memory (Spark, Storm) Graph + Search (Solr, Giraph) Enriched Customer Profile Modeling Scoring ButWhy Hadoop? Reason #3 - Processing Frameworks •Hadoop started by being synonymous with MapReduce, and Java coding •But YARN (Yet another Resource Negotiator) broke this dependency •Modern Hadoop platforms provide overall cluster resource management, but support multiple processing frameworks •General-purpose (e.g. MapReduce) •Graph processing •Machine Learning •Real-Time Processing (Spark Streaming, Storm) •Even the Hadoop resource management framework can be swapped out •Apache Mesos
- 36. info@rittmanmead.com www.rittmanmead.com @rittmanmead CombineWith DW for Old-World/New-World Solution
- 37. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Mosthigh-end RDBMS vendors provide connectors to load data in/out of Hadoop platforms ‣Bulk extract ‣External tables ‣Query federation •Use high-end RDBMSs as specialist engines •a.k.a. "Data Marts" But … Analytic RDBMSs Are The New Data Mart Discovery & Development Labs Safe & secure Discovery and Development environment Data Warehouse Curated data : Historical view and business aligned access Business Intelligence Tools Big Data Management Platform Data sets and samples Models and programs Big Data Platform - All Running Natively Under Hadoop YARN (Cluster Resource Management) Batch (MapReduce) HDFS (Cluster Filesystem holding raw data) Interactive (Impala, Drill, Tez, Presto) Streaming + In-Memory (Spark, Storm) Graph + Search (Solr, Giraph) Enriched Customer Profile Modeling Scoring
- 38. BI Innovation ishappening around Hadoop
- 39. BI Innovation ishappening around Hadoop
- 40.
- 41.
- 42. too slow for ad-hocquerying?
- 44.
- 47.
- 49. Hadoop is nowfast
- 50. info@rittmanmead.com www.rittmanmead.com @rittmanmead56 Hadoop 2.0 Processing Frameworks + Tools
- 51. info@rittmanmead.com www.rittmanmead.com @rittmanmead57 •Cloudera’s answer to Hive query response time issues •MPP SQL query engine running on Hadoop, bypasses MapReduce for direct data access •Mostly in-memory, but spills to disk if required •Uses Hive metastore to access Hive table metadata •Similar SQL dialect to Hive - not as rich though and no support for Hive SerDes, storage handlers etc Cloudera Impala - Fast, MPP-style Access to Hadoop Data
- 52. info@rittmanmead.com www.rittmanmead.com @rittmanmead58 •Beginners usually store data in HDFS using text file formats (CSV) but these have limitations •Apache AVRO often used for general-purpose processing ‣Splitability, schema evolution, in-built metadata, support for block compression •Parquet now commonly used with Impala due to column-orientated storage ‣Mirrors work in RDBMS world around column-store ‣Only return (project) the columns you require across a wide table Apache Parquet - Column-Orientated Storage for Analytics
- 53. info@rittmanmead.com www.rittmanmead.com @rittmanmead59 •But Parquet (and HDFS) have significant limitation for real-time analytics applications ‣Append-only orientation, focus on column-store makes streaming ingestion harder •Cloudera Kudu aims to combine best of HDFS + HBase ‣Real-time analytics-optimised ‣Supports updates to data ‣Fast ingestion of data ‣Accessed using SQL-style tables and get/put/update/delete API Cloudera Kudu - Combining Best of HBase and Column-Store
- 54. info@rittmanmead.com www.rittmanmead.com @rittmanmead60 •Kudu storage used with Impala - create tables using Kudu storage handler •Can now UPDATE, DELETE and INSERT into Hadoop tables, not just SELECT and LOAD DATA Example Impala DDL + DML Commands with Kudu CREATE TABLE `my_first_table` ( `id` BIGINT, `name` STRING ) TBLPROPERTIES( 'storage_handler' = 'com.cloudera.kudu.hive.KuduStorageHandler', 'kudu.table_name' = 'my_first_table', 'kudu.master_addresses' = 'kudu-master.example.com:7051', 'kudu.key_columns' = 'id' ); INSERT INTO my_first_table VALUES (99, "sarah"); INSERT IGNORE INTO my_first_table VALUES (99, "sarah"); UPDATE my_first_table SET name="bob" where id = 3; DELETE FROM my_first_table WHERE id < 3; DELETE c FROM my_second_table c, stock_symbols s WHERE c.name = s.symbol;
- 55. and it’s nowin-memory
- 57. info@rittmanmead.com www.rittmanmead.com @rittmanmead63 •Another DAG execution engine running on YARN •More mature than TEZ, with richer API and more vendor support •Uses concept of an RDD (Resilient Distributed Dataset) ‣RDDs like tables or Pig relations, but can be cached in-memory ‣Great for in-memory transformations, or iterative/cyclic processes •Spark jobs comprise of a DAG of tasks operating on RDDs •Access through Scala, Python or Java APIs •Related projects include ‣Spark SQL ‣Spark Streaming Apache Spark
- 58. info@rittmanmead.com www.rittmanmead.com @rittmanmead64 •Spark SQL, and Data Frames, allow RDDs in Spark to be processed using SQL queries •Bring in and federate additional data from JDBC sources •Load, read and save data in Hive, Parquet and other structured tabular formats Spark SQL - Adding SQL Processing to Apache Spark val accessLogsFilteredDF = accessLogs .filter( r => ! r.agent.matches(".*(spider|robot|bot|slurp).*")) .filter( r => ! r.endpoint.matches(".*(wp-content|wp-admin).*")).toDF() .registerTempTable("accessLogsFiltered") val topTenPostsLast24Hour = sqlContext.sql("SELECT p.POST_TITLE, p.POST_AUTHOR, COUNT(*) as total FROM accessLogsFiltered a JOIN posts p ON a.endpoint = p.POST_SLUG GROUP BY p.POST_TITLE, p.POST_AUTHOR ORDER BY total DESC LIMIT 10 ") // Persist top ten table for this window to HDFS as parquet file topTenPostsLast24Hour.save("/user/oracle/rm_logs_batch_output/topTenPostsLast24Hour.parquet" , "parquet", SaveMode.Overwrite)
- 59. info@rittmanmead.com www.rittmanmead.com @rittmanmead65 Accompanied by Innovations in Underlying Platform Cluster Resource Management to support multi-tenant distributed services In-Memory Distributed Storage, to accompany In-Memory Distributed Processing
- 60. Dataflow Pipelines arethe new ETL
- 61. New ways todo BI
- 62. New ways todo BI
- 63. Hadoop is thenew ETL Engine
- 64. info@rittmanmead.com www.rittmanmead.com @rittmanmead Copyright© 2015, Oracle and/or its affiliates. All rights reserved. | Proprietary ETL engines die circa 2015 – folded into big data Oracle Open World 2015 21 Proprietary ETL is Dead. Apache-based ETL is What’s Next Scripted SQL Stored Procs ODI for Columnar ODI for In-Mem ODI for Exadata ODI for Hive ODI for Pig & Oozie 1990’s Eon of Scripts and PL-SQL Era of SQL E-LT/Pushdown Big Data ETL in Batch Streaming ETL Period of Proprietary Batch ETL Engines Informatica Ascential/IBM Ab Initio Acta/SAP SyncSort 1994 Oracle Data Integrator ODI for Spark ODI for Spark Streaming Warehouse Builder
- 65. Machine Learning &Search for “Automagic” Schema Discovery
- 66. New ways todo BI
- 67. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Bydefinition there's lots of data in a big data system ... so how do you find the data you want? •Google's own internal solution - GOODS ("Google Dataset Search") •Uses crawler to discover new datasets •ML classification routines to infer domain •Data provenance and lineage •Indexes and catalogs 26bn datasets •Other users, vendors also have solutions •Oracle Big Data Discovery •Datameer •Platfora •Cloudera Navigator Google GOODS - Catalog + Search At Google-Scale
- 68. A New Takeon BI
- 69. info@rittmanmead.com www.rittmanmead.com @rittmanmead •Cameout if the data science movement, as a way to "show workings" •A set of reproducible steps that tell a story about the data •as well as being a better command-line environment for data analysis •One example is Jupyter, evolution of iPython notebook •supports pySpark, Pandas etc •See also Apache Zepplin Web-Based Data Analysis Notebooks
- 70. Meanwhile in the realworld … https://www.youtube.com/watch?v=h1UmdvJDEYY
- 71. And Emerging Open-Source BITools and Platforms
- 72. And Emerging Open-Source BITools and Platforms http://larrr.com/wp-content/uploads/2016/05/paper.pdf
- 74. And Emerging Open-Source BITools and Platforms
- 76. To see anexample:
- 77. See an examplein action: https://speakerdeck.com/markrittman/oracle-big-data-discovery-extending-into-machine-learning-a-quantified-self-case-study
- 78.
- 79. EVENT SPEAKER DANISH BIMEETUP, SEP’ 2016 FROM LOTS OF REPORTS (WITH SOME DATA ANALYSIS) TO MASSIVE DATA ANALYSIS (WITH SOME REPORTING) MARK RITTMAN, ORACLE ACE DIRECTOR