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

[email protected] www.rittmanmead.com @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

[email protected] www.rittmanmead.com @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 :http://www.rittmanmead.com/blog

•Email : [email protected]•Twitter : @markrittman

About the Speaker

mailto:[email protected]


•Everyone’s talking about Hadoop and “Big Data”

Hadoop is the Big Hot Topic In IT / Analytics


•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?


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


•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”

DataTransfer DataAccess

DataFactory DataReservoir

BusinessIntelligenceTools

HadoopPlatform

FileBasedIntegration

StreamBased

Integration

Datastreams

Discovery&DevelopmentLabsSafe&secureDiscoveryandDevelopment

environment

Datasetsandsamples

Models andprograms

Marketing/SalesApplications

Models

MachineLearning

Segments

OperationalData

Transactions

CustomerMasterata

UnstructuredData

Voice+ChatTranscripts

ETLBasedIntegration

RawCustomerData

Datastoredintheoriginal

format(usuallyfiles)suchasSS7,ASN.1,JSONetc.

MappedCustomerData

Datasetsproducedbymappingandtransformingrawdata


Incorporate Hadoop Data Reservoirs into DW Design

Virtu

aliz

atio

n &

Q

uery

Fed

erat

ion

Enterprise Performance Management

Pre-built & Ad-hoc BI Assets

Information Services

Data Ingestion

Information Interpretation

Access & Performance Layer

Foundation Data Layer

Raw Data Reservoir

Data Science

Data Engines & Poly-structured 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


•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

Enriched Customer Profile

Modeling

Scoring

Infiniband


•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



Reducer Aggregate

Reducer Aggregate

Output One HDFS file per reducer,in a directory


•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, automaticallycreates MapReduce jobs against data previously loaded into the Hive HDFS tables

•Approach used by ODI and OBIEEto 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


•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-structureddata 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?


•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

Hadoop is slowand only for batch jobs

…isn’t it?

but …

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 : [email protected] W : www.rittmanmead.com

Hadoop is Now Real-Time, In-Memory and Analytics-Optimised


•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


•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


•MapReduce 2 (MR2) splits the functionality of the JobTrackerby 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


•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

Inpu

t Dat

a

TEZ DAGMap()

Map()

Map()

Reduce()

Out

put D

ata

Reduce()

Reduce()

Reduce()

Inpu

t Dat

a Map()

Map()

Reduce()

Reduce()


Tez Advantage - Drop-In Replacement for MR with Hive, Pig

set hive.execution.engine=mr

set hive.execution.engine=tez

4m 17s

2m 25s


•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


•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


Accompanied by Innovations in Underlying Platform

Cluster Resource Management tosupport multi-tenant distributed services

In-Memory Distributed Storage,to accompany In-Memory Distributed Processing


•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


Call Center LogsChat Logs iBeacon Logs Website Logs

Real-time Feeds

Raw Data


•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


•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


•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 useFlume as a consumer of messages‣But gaining popularity, especially alongside Spark Streaming

Apache Kafka : Reliable, Message-Based


•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:http://blog.cloudera.com/blog/2014/11/how-to-do-near-real-time-sessionization-with-spark-streaming-and-apache-hadoop/

Adding Real-Time Processing to Loading : Spark Streaming

http://blog.cloudera.com/blog/2014/11/how-to-do-near-

Hadoop developmentis only for Java programmers

…isn’t it?

but …

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 : [email protected] W : www.rittmanmead.com

SQL Increasingly Used in Hadoop for Data Access


•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


•A replacement for Hive, but uses Hive concepts anddata dictionary (metastore)

•MPP (Massively Parallel Processing) query enginethat 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-timeSQL-like access to Hadoop

How Impala Works

Impala

Hadoop

HDFS etc

BI Server

Presentation Svr

Cloudera ImpalaODBC Driver

Impala

Hadoop

HDFS etc

Impala

Hadoop

HDFS etc

Impala

Hadoop

HDFS etc

Impala

Hadoop

HDFS etc

Multi-NodeHadoop Cluster


•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


•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


•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

HadoopCluster

Exadata DatabaseServer

Oracle Big Data SQL

SQL Queries

SmartScan SmartScan


•Oracle Database 12c 12.1.0.2.0 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


•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(com.oracle.bigdata.tablename=default.access_per_post_categories));


•Brings query-offloading features of Exadatato Oracle Big Data Appliance

•Query across both Oracle and Hadoop sources• Intelligent query optimisation applies SmartScanclose 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


•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 flexibilityvs 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 | +------------+------------+--------------+


•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 optimizedstorage 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)


•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)


Choosing the Appropriate SQL Engine to Add to Hadoop


•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


•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 tablesand get/put/update/delete API

Cloudera Kudu - Combining Best of HBase and Column-Store

Hadoop is insecureand has fragmented security

…doesn’t it?

but …


Consistent Security and Audit Now Emerging on Platform


•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


•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 Oraclesecurity model over Hadoop datasets

Oracle Big Data SQL : Extend Oracle Security to Hadoop

Redacteddatasubset

SQLJSON

CustomerdatainOracleDB

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'', ''REDACTION_TESTER'')=''TRUE''' );


•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

any predictive modellinghas to be done outside Hadoop, in R

…doesn’t it?

but …


•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 = test.map { 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 model.save(sc, "myModelPath") val sameModel = SVMModel.load(sc, "myModelPath")


•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 requiredto 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


Example Usage : Oracle Big Data Preparation Cloud Service


Identifying Schemas in Semi-/Unstructured Data


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


•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 11.1.1.9, 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


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

Mark Rittman, CTO, Rittman Mead Riga Dev Day 2016, Riga, March 2016

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

Data & Analytics

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