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Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive Analytics


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This is a session for Oracle DBAs and devs that looks at the cutting edge big data techs like Spark, Kafka etc, and through demos shows how Hadoop is now a a real-time platform for fast analytics, data integration and predictive modeling

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Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive Analytics

  1. 1. @rittmanmead Big Data for Oracle Developers & DBAs - 
 Towards Spark, Real-Time and Predictive Analytics Mark Rittman, CTO, Rittman Mead Riga Dev Day 2016, Riga, March 2016
  2. 2. @rittmanmead 2 •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 : •Email : •Twitter : @markrittman About the Speaker
  3. 3. @rittmanmead •Everyone’s talking about Hadoop and “Big Data” Hadoop is the Big Hot Topic In IT / Analytics
  4. 4. @rittmanmead •Gives us an ability to store more data, at more detail, for longer •Provides a cost-effective way to analyse vast amounts of data •Hadoop & NoSQL technologies can give us “schema-on-read” capabilities •There’s vast amounts of innovation in this area we can harness •And it’s very complementary to Oracle BI & DW Why is Hadoop of Interest to Us?
  5. 5. @rittmanmead Flexible Cheap Storage for Logs, Feeds + Social Data $50k Hadoop Node Voice + Chat Transcripts Call Center LogsChat Logs iBeacon Logs Website LogsCRM Data Transactions Social FeedsDemographics Raw Data Customer 360 Apps Predictive 
 Models SQL-on-Hadoop Business analytics Real-time Feeds,
 batch and API
  6. 6. @rittmanmead •Extend the DW with new data sources, datatypes, detail-level data •Offload archive data into Hadoop but federate it with DW data in user queries •Use Hadoop, Hive and MapReduce for low-cost ETL staging Deploy Alongside Traditional DW as “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
  7. 7. @rittmanmead Incorporate Hadoop Data Reservoirs into DW Design Virtualization&
 QueryFederation Enterprise Performance Management Pre-built & 
 BI Assets Information
 Services Data Ingestion Information Interpretation Access & Performance Layer Foundation Data Layer Raw Data Reservoir Data 
 Science Data Engines & 
 sources Content Docs Web & Social Media SMS Structured Data
 Sources •Operational Data •COTS Data •Master & Ref. Data •Streaming & BAM Immutable raw data reservoir Raw data at rest is not interpreted Immutable modelled data. Business Process Neutral form. Abstracted from business process changes Past, current and future interpretation of enterprise data. Structured to support agile access & navigation Discovery Lab Sandboxes Rapid Development Sandboxes Project based data stores to support specific discovery objectives Project based data stored to facilitate rapid content / presentation delivery Data Sources
  8. 8. @rittmanmead 8 •Oracle Engineered system for big data processing and analysis •Start with Oracle Big Data Appliance Starter Rack - expand up to 18 nodes per rack •Cluster racks together for horizontal scale-out using enterprise-quality infrastructure Oracle Big Data Appliance Starter Rack + Expansion • Cloudera CDH + Oracle software • 18 High-spec Hadoop Nodes with InfiniBand switches for internal Hadoop traffic, optimised for network throughput • 1 Cisco Management Switch • Single place for support for H/W + S/ W
 Deployed on Oracle Big Data Appliance Oracle Big Data Appliance Starter Rack + Expansion • Cloudera CDH + Oracle software • 18 High-spec Hadoop Nodes with InfiniBand switches for internal Hadoop traffic, optimised for network throughput • 1 Cisco Management Switch • Single place for support for H/W + S/ W
 Customer Profile Modeling Scoring Infiniband
  9. 9. @rittmanmead •Hadoop, through MapReduce, breaks processing down into simple stages ‣Map : select the columns and values you’re interested in, pass through as key/value pairs ‣Reduce : aggregate the results •Most ETL jobs can be broken down into filtering, 
 projecting and aggregating •Hadoop then automatically runs job on cluster ‣Share-nothing small chunks of work ‣Run the job on the node where the data is ‣Handle faults etc ‣Gather the results back in Hadoop Tenets : Simplified Distributed Processing Mapper Filter, Project Mapper Filter, Project Mapper Filter, Project Reducer Aggregate Reducer Aggregate Output
 One HDFS file per reducer,
 in a directory
  10. 10. @rittmanmead •MapReduce jobs are typically written in Java, but Hive can make this simpler •Hive is a query environment over Hadoop/MapReduce to support SQL-like queries •Hive server accepts HiveQL queries via HiveODBC or HiveJDBC, automatically
 creates MapReduce jobs against data previously loaded into the Hive HDFS tables •Approach used by ODI and OBIEE
 to gain access to Hadoop data •Allows Hadoop data to be accessed just like 
 any other data source (sort of...) Hive as the Hadoop SQL Access Layer
  11. 11. @rittmanmead •Data integration tools such as Oracle Data Integrator can load and process Hadoop data •BI tools such as Oracle Business Intelligence 12c can report on Hadoop data •Generally use MapReduce and Hive to access data ‣ODBC and JDBC access to Hive tabular data ‣Allows Hadoop unstructured/semi-structured
 data on HDFS to be accessed like RDBMS Hive Provides a SQL Interface for BI + ETL Tools Access direct Hive or extract using ODI12c for structured OBIEE dashboard analysis What pages are people visiting? Who is referring to us on Twitter? What content has the most reach?
  12. 12. @rittmanmead •Most Oracle DBAs and developers know about Hadoop, but assume… Common Developer Understanding of Hadoop Today ‣Hadoop is just for batch (because of the MapReduce JVN spin-up issue) ‣Hadoop is just for large datasets, not ad-hoc work or micro batches ‣Hadoop will always be slow because it stages everything to disk ‣All Hadoop can do is Map (select, filter) and Reduce (aggregate) ‣Hadoop == MapReduce
  13. 13. Hadoop is slow and only for batch jobs …isn’t it? but …
  14. 14. T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : W : Hadoop is Now Real-Time, In-Memory and Analytics-Optimised
  15. 15. @rittmanmead 15 •MapReduce’s great innovation was to break processing down into distributed jobs •Jobs that have no functional dependency on each other, only upstream tasks •Provides a framework that is infinitely scalable and very fault tolerant •Hadoop handled job scheduling and resource management ‣All MapReduce code had to do was provide the “map” and “reduce” functions ‣Automatic distributed processing ‣Slow but extremely powerful Hadoop 1.0 and MapReduce
  16. 16. @rittmanmead 16 •A typical Hive or Pig script compiles down into multiple MapReduce jobs •Each job stages its intermediate results to disk •Safe, but slow - write to disk, spin-up separate JVMs for each job Compiling Hive/Pig Scripts into MapReduce register /opt/cloudera/parcels/CDH/lib/pig/piggybank.jar raw_logs = LOAD '/user/mrittman/rm_logs' USING TextLoader AS (line:chararray); logs_base = FOREACH raw_logs GENERATE FLATTEN (REGEX_EXTRACT_ALL(line,'^(S+) (S+) (S+) [([w:/]+s[+-]d{4})] 
 "(.+?)" (S+) (S+) "([^"]*)" "([^"]*)"') )AS (remoteAddr: chararray, remoteLogname: chararray, user: chararray,time: chararray); logs_base_nobots = FILTER logs_base BY NOT (browser matches '.*(spider|robot|bot|slurp|bot.*'); logs_base_page = FOREACH logs_base_nobots GENERATE SUBSTRING(time,0,2) as day) 
 AS (method:chararray, request_page:chararray, protocol:chararray), remoteAddr, status; logs_base_page_cleaned = FILTER logs_base_page BY NOT (SUBSTRING(request_page,0,3) == 
 '/wp' or request_page == '/' or SUBSTRING(request_page,0,7) == '/files/' 
 or SUBSTRING(request_page,0,12) == '/favicon.ico'); logs_base_page_cleaned_by_page = GROUP logs_base_page_cleaned BY request_page; page_count = FOREACH logs_base_page_cleaned_by_page GENERATE FLATTEN(group) 
 as request_page, COUNT(logs_base_page_cleaned) as hits; … store pages_and_post_top_10 into 'top_10s/pages'; JobId Maps Reduces Alias Feature Outputs job_1417127396023_0145 12 2 logs_base,logs_base_nobots,logs_base_page,logs_base_page_cleaned,
 logs_base_page_cleaned_by_page,page_count,raw_logs GROUP_BY,COMBINER job_1417127396023_0146 2 1 pages_and_post_details,pages_and_posts_trim,posts,posts_cleaned HASH_JOIN job_1417127396023_0147 1 1 pages_and_posts_sorted SAMPLER job_1417127396023_0148 1 1 pages_and_posts_sorted ORDER_BY,COMBINER job_1417127396023_0149 1 1 pages_and_posts_sorted
  17. 17. @rittmanmead 17 •MapReduce 2 (MR2) splits the functionality of the JobTracker
 by separating resource management and job scheduling/monitoring •Introduces YARN (Yet Another Resource Manager) •Permits other processing frameworks to MR ‣For example, Apache Spark •Maintains backwards compatibility with MR1 •Introduced with CDH5+ MapReduce 2 and YARN Node
 Manager Node
 Manager Node
 Manager Resource
 Manager Client Client
  18. 18. @rittmanmead 18 •Runs on top of YARN, provides a faster execution engine than MapReduce for Hive, Pig etc •Models processing as an entire data flow graph (DAG), rather than separate job steps ‣DAG (Directed Acyclic Graph) is a new programming style for distributed systems ‣Dataflow steps pass data between them as streams, rather than writing/reading from disk •Supports in-memory computation, enables Hive on Tez (Stinger) and Pig on Tez •Favoured In-memory / Hive v2 
 route by Hortonworks Apache Tez InputData TEZ DAG Map() Map() Map() Reduce() OutputData Reduce() Reduce() Reduce() InputData Map() Map() Reduce() Reduce()
  19. 19. @rittmanmead 19 Tez Advantage - Drop-In Replacement for MR with Hive, Pig set hive.execution.engine=mr set hive.execution.engine=tez 4m 17s 2m 25s
  20. 20. @rittmanmead 22 •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
  21. 21. @rittmanmead 23 •Native support for multiple languages 
 with identical APIs ‣Python - prototyping, data wrangling ‣Scala - functional programming features ‣Java - lower-level, application integration •Use of closures, iterations, and other 
 common language constructs to minimize code •Integrated support for distributed +
 functional programming •Unified API for batch and streaming Rich Developer Support + Wide Developer Ecosystem scala> val logfile = sc.textFile("logs/access_log") 14/05/12 21:18:59 INFO MemoryStore: ensureFreeSpace(77353) 
 called with curMem=234759, maxMem=309225062 14/05/12 21:18:59 INFO MemoryStore: Block broadcast_2 
 stored as values to memory (estimated size 75.5 KB, free 294.6 MB) logfile: org.apache.spark.rdd.RDD[String] = 
 MappedRDD[31] at textFile at <console>:15 scala> logfile.count() 14/05/12 21:19:06 INFO FileInputFormat: Total input paths to process : 1 14/05/12 21:19:06 INFO SparkContext: Starting job: count at <console>:1 ... 14/05/12 21:19:06 INFO SparkContext: Job finished: 
 count at <console>:18, took 0.192536694 s res7: Long = 154563 scala> val logfile = sc.textFile("logs/access_log").cache scala> val biapps11g = logfile.filter(line => line.contains("/biapps11g/")) biapps11g: org.apache.spark.rdd.RDD[String] = FilteredRDD[34] at filter at <console>:17 scala> biapps11g.count() ... 14/05/12 21:28:28 INFO SparkContext: Job finished: count at <console>:20, took 0.387960876 s res9: Long = 403
  22. 22. @rittmanmead 24 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
  23. 23. @rittmanmead 25 •Most Oracle DWs process data in batches (or at best, micro-batches) •Tools like ODI typically work in this way, 
 often linking up with database CDC •Hadoop systems are usually real-time, from the start ‣In the past, via Hadoop streaming, Flume etc ‣Batch loading then added for initial data load into system Combining Real-Time Processing with Real-Time Loading Hadoop Node Voice + Chat Transcripts Call Center LogsChat Logs iBeacon Logs Website Logs Real-time Feeds Raw Data
  24. 24. @rittmanmead 26 •Apache Flume is the standard way to transport log files from source through to target ‣Initial use-case was webserver log files, but can transport any file from A>B ‣Does not do data transformation, but can send to multiple targets / target types ‣Mechanisms and checks to ensure successful transport of entries •Has a concept of “agents”, “sinks” and “channels” ‣Agents collect and forward log data ‣Sinks store it in final destination ‣Channels store log data en-route •Simple configuration through INI files ‣Handled outside of ODI12c Apache Flume : Distributed Transport for Log Activity
  25. 25. @rittmanmead 27 •Oracle GoldenGate is also an option, for streaming RDBMS transactions to Hadoop •Leverages GoldenGate & HDFS / Hive Java APIs •Sample Implementations on MOS Doc.ID 1586210.1 (HDFS) and 1586188.1 (Hive) •Likely to be formal part of GoldenGate in future release - but usable now •Can also integrate with Flume for delivery to HDFS - see MOS Doc.ID 1926867.1 GoldenGate for Continuous Streaming to Hadoop
  26. 26. @rittmanmead 28 •Developed by LinkedIn, designed to address Flume issues around reliability, throughput ‣(though many of those issues have been addressed since) •Designed for persistent messages as the common use case ‣Website messages, events etc vs. log file entries •Consumer (pull) rather than Producer (push) model •Supports multiple consumers per message queue •More complex to set up than Flume, and can use
 Flume as a consumer of messages ‣But gaining popularity, especially 
 alongside Spark Streaming Apache Kafka : Reliable, Message-Based
  27. 27. @rittmanmead 29 •Add mid-stream processing to ingestion process •Sessionization, classification, more complex transformation and ref data lookup •Access to machine learning algorithms using MLib ‣Example implementation at:
 apache-hadoop/ Adding Real-Time Processing to Loading : Spark Streaming
  28. 28. Hadoop development is only for Java programmers …isn’t it? but …
  29. 29. T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or 
 +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : W : SQL Increasingly Used in Hadoop for Data Access
  30. 30. @rittmanmead 32 •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
  31. 31. @rittmanmead 33 •A replacement for Hive, but uses Hive concepts and
 data dictionary (metastore) •MPP (Massively Parallel Processing) query engine
 that runs within Hadoop ‣Uses same file formats, security,
 resource management as Hadoop •Processes queries in-memory •Accesses standard HDFS file data •Option to use Apache AVRO, RCFile,
 LZO or Parquet (column-store) •Designed for interactive, real-time
 SQL-like access to Hadoop How Impala Works Impala Hadoop HDFS etc BI Server Presentation Svr Cloudera Impala
 ODBC Driver Impala Hadoop HDFS etc Impala Hadoop HDFS etc Impala Hadoop HDFS etc Impala Hadoop HDFS etc Multi-Node
 Hadoop Cluster
  32. 32. @rittmanmead 34 •Log into Impala Shell, run INVALIDATE METADATA command to refresh Impala table list •Run SHOW TABLES Impala SQL command to view tables available •Run COUNT(*) on main ACCESS_PER_POST table to see typical response time Enabling Hive Tables for Impala [oracle@bigdatalite ~]$ impala-shell Starting Impala Shell without Kerberos authentication [bigdatalite.localdomain:21000] > invalidate metadata; Query: invalidate metadata Fetched 0 row(s) in 2.18s [bigdatalite.localdomain:21000] > show tables; Query: show tables +-----------------------------------+ | name | +-----------------------------------+ | access_per_post | | access_per_post_cat_author | | … | | posts | |——————————————————————————————————-+ Fetched 45 row(s) in 0.15s [bigdatalite.localdomain:21000] > select count(*) 
 from access_per_post; Query: select count(*) from access_per_post +----------+ | count(*) | +----------+ | 343 | +----------+ Fetched 1 row(s) in 2.76s
  33. 33. @rittmanmead 35 •Significant improvement over Hive response time •Now makes Hadoop suitable for ad-hoc querying Significantly-Improved Ad-Hoc Query Response Time vs Hive | Logical Query Summary Stats: Elapsed time 2, Response time 1, Compilation time 0 (seconds) Logical Query Summary Stats: Elapsed time 50, Response time 49, Compilation time 0 (seconds) Simple Two-Table Join against Hive Data Only Simple Two-Table Join against Impala Data Only vs
  34. 34. @rittmanmead 36 •Part of Oracle Big Data 4.0 (BDA-only) ‣Also requires Oracle Database 12c, Oracle Exadata Database Machine •Extends Oracle Data Dictionary to cover Hive •Extends Oracle SQL and SmartScan to Hadoop •Extends Oracle Security Model over Hadoop ‣Fine-grained access control ‣Data redaction, data masking ‣Uses fast c-based readers where possible
 (vs. Hive MapReduce generation) ‣Map Hadoop parallelism to Oracle PQ ‣Big Data SQL engine works on top of YARN ‣Like Spark, Tez, MR2 Oracle Big Data SQL Exadata
 Storage Servers Hadoop
 Cluster Exadata Database
 Server Oracle Big
 Data SQL SQL Queries SmartScan SmartScan
  35. 35. @rittmanmead 37 •Oracle Database 12c with Big Data SQL option can view Hive table metadata ‣Linked by Exadata configuration steps to one or more BDA clusters •DBA_HIVE_TABLES and USER_HIVE_TABLES exposes Hive metadata •Oracle SQL*Developer 4.0.3, with Cloudera Hive drivers, can connect to Hive metastore View Hive Table Metadata in the Oracle Data Dictionary SQL> col database_name for a30 SQL> col table_name for a30 SQL> select database_name, table_name 2 from dba_hive_tables; DATABASE_NAME TABLE_NAME ------------------------------ ------------------------------ default access_per_post default access_per_post_categories default access_per_post_full default apachelog default categories default countries default cust default hive_raw_apache_access_log
  36. 36. @rittmanmead 38 •Big Data SQL accesses Hive tables through external table mechanism ‣ORACLE_HIVE external table type imports Hive metastore metadata ‣ORACLE_HDFS requires metadata to be specified •Access parameters cluster and tablename specify Hive table source and BDA cluster Hive Access through Oracle External Tables + Hive Driver CREATE TABLE access_per_post_categories( hostname varchar2(100), request_date varchar2(100), post_id varchar2(10), title varchar2(200), author varchar2(100), category varchar2(100), ip_integer number) organization external (type oracle_hive default directory default_dir access parameters(;
  37. 37. @rittmanmead 39 •Brings query-offloading features of Exadata
 to Oracle Big Data Appliance •Query across both Oracle and Hadoop sources •Intelligent query optimisation applies SmartScan
 close to ALL data •Use same SQL dialect across both sources •Apply same security rules, policies, 
 user access rights across both sources Extending SmartScan, and Oracle SQL, Across All Data
  38. 38. @rittmanmead 40 •SQL query engine that doesn’t require a formal (HCatalog) schema •Infers the schema from the semi-structured dataset (JSON etc) ‣Allows users to analyze data without any ETL or up-front schema definitions. ‣Data can be in any file format such as text, JSON, or Parquet ‣Improved agility and flexibility
 vs formal modelling in Hive etc Apache Drill 0: jdbc:drill:zk=local> select state, city, count(*) totalreviews from dfs.`/<path-to-yelp-dataset>/yelp/yelp_academic_dataset_business.json` group by state, city order by count(*) desc limit 10; +------------+------------+--------------+ | state | city | totalreviews | +------------+------------+--------------+ | NV | Las Vegas | 12021 | | AZ | Phoenix | 7499 | | AZ | Scottsdale | 3605 | | EDH | Edinburgh | 2804 | | AZ | Mesa | 2041 | | AZ | Tempe | 2025 | | NV | Henderson | 1914 | | AZ | Chandler | 1637 | | WI | Madison | 1630 | | AZ | Glendale | 1196 | +------------+------------+--------------+
  39. 39. @rittmanmead 41 •Addition of Spark as a back-end execution engine for Hive (and Pig) •Has the advantage of making use of all existing Hive scripts, infrastructure •But … probably is even more of a dead-end than Tez ‣Is still faster than Hive on MR ‣But Hive with column/in-memory optimized
 storage is now typically CPU bound ‣Spark consumes more CPU, Disk 
 & Network IO than Tez ‣Additional translation overhead from 
 RDDs to Hive’s “Row Containers” Hive-on-Spark (and Pig-on-Spark)
  40. 40. @rittmanmead 42 •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 
 ORDER BY total DESC LIMIT 10 ") // Persist top ten table for this window to HDFS as parquet file"/user/oracle/rm_logs_batch_output/topTenPostsLast24Hour.parquet"
 , "parquet", SaveMode.Overwrite)
  41. 41. @rittmanmead 43 Choosing the Appropriate SQL Engine to Add to Hadoop
  42. 42. @rittmanmead 44 •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
  43. 43. @rittmanmead 45 •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
  44. 44. Hadoop is insecure and has fragmented security …doesn’t it? but …
  45. 45. @rittmanmead 47 Consistent Security and Audit Now Emerging on Platform
  46. 46. @rittmanmead 48 •Clusters by default are unsecured (vunerable to account spoofing) & need Kerberos enabled •Data access controlled by POSIX-style permissions on HDFS files •Hive and Impala can Apache Sentry RBAC ‣Result is data duplication and complexity ‣No consistent API or abstracted security model Hadoop Security Initially Was a Mess /user/mrittman/scratchpad /user/ryeardley/scratchpad /user/mpatel/scratchpad /user/mrittman/scratchpad /user/mrittman/scratchpad /data/rm_website_analysis/logfiles/incoming /data/rm_website_analysis/logfiles/archive /data/rm_website_analysis/tweets/incoming /data/rm_website_analysis/tweets/archive
  47. 47. @rittmanmead 49 •Use standard Oracle Security over Hadoop & NoSQL ‣Grant & Revoke Privileges ‣Redact Data ‣Apply Virtual Private Database ‣Provides Fine-grain Access Control •Great solution to extend existing Oracle
 security model over Hadoop datasets Oracle Big Data SQL : Extend Oracle Security to Hadoop Redacted data subset SQL JSON Customer data in Oracle DB DBMS_REDACT.ADD_POLICY( object_schema => 'txadp_hive_01', object_name => 'customer_address_ext', column_name => 'ca_street_name', policy_name => 'customer_address_redaction', function_type => DBMS_REDACT.RANDOM, expression => 'SYS_CONTEXT(''SYS_SESSION_ROLES'', 
  48. 48. @rittmanmead 50 •Provides a higher level, logical abstraction for data (ie Tables or Views) ‣Can be used with Spark & Spark SQL, with Predicate pushdown, projection •Returns schemed objects (instead of paths and bytes) in similar way to HCatalog •Unified data access path allows platform-wide performance improvements •Secure service that does not execute arbitrary user code ‣Central location for all authorization checks using Sentry metadata. Cloudera RecordService
  49. 49. any predictive modelling has to be done outside Hadoop, in R …doesn’t it? but …
  50. 50. @rittmanmead 52 •Part of Spark, extends Scala, Java & Python API •Integrated workflow including ML pipelines •Currently supports following algorithms: ‣Binary classification ‣Regression ‣Clustering ‣Collaborative filtering ‣Dimensionality Reduction Spark MLLib : Adding Machine Learning Capabilities to Spark // Compute raw scores on the test set. val scoreAndLabels = { point => val score = model.predict(point.features) (score, point.label) } // Get evaluation metrics. val metrics = new BinaryClassificationMetrics(scoreAndLabels) val auROC = metrics.areaUnderROC() println("Area under ROC = " + auROC) // Save and load model, "myModelPath") val sameModel = SVMModel.load(sc, "myModelPath")
  51. 51. @rittmanmead 53 •Data enrichment tool aimed at domain experts, not programmers •Uses machine-learning to automate 
 data classification + profiling steps •Automatically highlight sensitive data,
 and offer to redact or obfuscate •Dramatically reduce the time required
 to onboard new data sources •Hosted in Oracle Cloud for zero-install ‣File upload and download from browser ‣Automate for production data loads Raw Data Data stored in the original format (usually files) such as SS7, ASN. 1, JSON etc. Mapped Data Data sets produced by mapping and transforming raw data Voice + Chat Transcripts Example Usage : Oracle Big Data Preparation Cloud Service
  52. 52. @rittmanmead 54 Identifying Schemas in Semi-/Unstructured Data
  53. 53. @rittmanmead 55 Use of Machine Learning to Identify Data Patterns •Automatically profile, parse and classify incoming datasets using Spark MLLib Word2Vec •Spot and obfuscate sensitive data automatically, automatically suggest column names
  54. 54. @rittmanmead 57 •Hadoop is evolving ‣Hadoop 2.0 breaks the dependency on MapReduce ‣Spark, Tez etc allow us to create execution plans that 
 run in-memory, faster than before ‣New streaming models allow us to process data 
 via sockets, micro batches or continuously •And Oracle developers can make use of these new capabilities ‣Oracle Big Data SQL can access Hadoop data loaded in real-time ‣OBIEE, particularly in, can access Impala ‣ODI is likely to support Hive on Tez and Hive on Spark shortly, 
 and will have support for Spark in the future Summary
  55. 55. @rittmanmead Big Data for Oracle Devs - 
 Towards Spark, Real-Time and Predictive Analytics Mark Rittman, CTO, Rittman Mead Riga Dev Day 2016, Riga, March 2016