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Bridging Oracle Database and Hadoop
Alex Gorbachev
October 2015
Alex Gorbachev• Chief Technology Officer at Pythian• Blogger• Cloudera Champion of Big Data• OakTable Network member• Oracle ACE Director• Founder of BattleAgainstAnyGuess.com• Founder of Sydney Oracle Meetup• EVP, IOUG
What is Big Data?
and why Big Data today?
Why Big Data boom now?• Advances in communication – it’s now feasible to
transfer large amounts of data economically by anyone from virtually anywhere
• Commodity hardware – high performance and high capacity at low price is available
• Commodity software – open-source phenomena made advanced software products affordable to anyone
• New data sources – mobile, sensors, social media data-sources
• What’s been only possible at very high cost in the past, can now be done by any small or large business
Big Data = Affordable at Scale
Not everyone is Facebook, Google, Yahoo and etc.
These guys had to push the envelope because traditional technology didn’t
scale
Not everyone is Facebook, Google, Yahoo and etc.
These guys had to push the envelope because traditional technology didn’t
scale
Mere mortals’ challenge is cost and agility
System capability per $Big Data technology may be expensive at low scale due to high engineering efforts.
Traditional technology becomes too complex and expensive to scale.
investments, $
capa
bilit
ies
traditional
Big Data
What is Hadoop?
Hadoop Design Principle #1Scalable Affordable Reliable Data Store
Cheap & Scalable
Simple
Specialized
Shared nothing
Single threaded
writes
Fault tolerance
HDFS – Hadoop Distributed Filesystem
Hadoop Design Principle #2Bring Code to Data
Code
Data
Why is Hadoop so affordable?• Cheap hardware• Resiliency through software• Horizontal scalability• Open-source software
How much does it cost?Oracle Big Data Appliance X5-2 rack - $525K list price• 18 data nodes• 648 CPU cores• 2.3 TB RAM• 216 x 4TB disks• 864TB of raw disk capacity• 288TB usable (triple mirror)• 40G InfiniBand + 10GbE
networking• Cloudera Enterprise
Hadoop is very flexible• Rich ecosystem of tools• Can handle any data format
– Relational– Text– Audio, video– Streaming data– Logs– Non-relational structured data (JSON, XML, binary
formats)– Graph data
• Not limited to relational data processing
Challenges with Hadoopfor those of us used to Oracle
• New data access tools– Relational and non-relational data
• Non-Oracle (and non-ANSI) Hive SQL– Java-based UDFs and UDAFs
• Security features are not there out-of-the-box
• Maybe slow for “small data”
Tables in Hadoop
using Hadoop with relational data abstractions
Apache Hive• Apache Hive provides a SQL layer over Hadoop
– data in HDFS (structured or unstructured via SerDe)– using one of distributed processing frameworks –
MapReduce, Spark, Tez• Presents data from HDFS as tables and columns
– Hive metastore (aka data dictionary)• SQL language access (HiveQL)
– Parses SQL and creates execution plans in MR, Spark or Tez
• JDBC and ODBC drivers– Access from ETL and BI tools– Custom apps– Development tools
Native Hadoop tools• Demo
• HUE– HDFS files– Hive– Impala
Access Hive using SQL Developer• Demo
• Use Cloudera JDBC drivers• Query data & browse metadata• Run DDL from SQL tab• Create Hive table definitions inside Oracle
DB
Hadoop and OBIEE 11g• OBIEE 11.1.1.7 can query Hive/Hadoop as
a data source– Hive ODBC drivers– Apache Hive Physical Layer database type
• Limited features– OBIEE 11.1.1.7 OBIEE has HiveServer1 ODBC
drivers– HiveQL is only a subset of ANSI SQL
• Hive query response time is slow for speed of thought response time
ODI 12c• ODI – data transformation tool
– ELT approach pushes transformations down to Hadoop - leveraging power of cluster
– Hive, HBase, Sqoop and OLH/ODCH KMs provide native Hadoop loading / transformation
• Upcoming support for Pig and Spark• Workflow orchestration• Metadata and model-driven• GUI workflow design• Transformation audit & data quality
Moving Data to Hadoop using ODI• Interface with Apache Sqoop using IKM SQL
to Hive-HBase-File knowledge module– Hadoop ecosystem tool– Able to run in parallel– Optimized Sqoop JDBC drivers integration for
Oracle– Bi-directional in-and-out of Hadoop to RDBMS– Data is moved directly between Hadoop cluster
and database• Export RBDMS data to file and load using
IKM File to Hive
Integrating Hadoop with Oracle Database
Oracle Big Data Connectors• Oracle Loader for Hadoop
– Offloads some pre-processing to Hadoop MR jobs (data type conversion, partitioning, sorting).
– Direct load into the database (online method)– Data Pump binary files in HDFS (offline method)
• These can then be accessed as external tables on HDFS
• Oracle Direct Connector for Hadoop– Create external table on files in HDFS– Text files or Data Pump binary files– WARNING: lots of data movement! Great for
archival non-frequently accessed data to HDFS
Oracle Big Data SQL
25
Source: http://www.slideshare.net/gwenshap/data-wrangling-and-oracle-connectors-for-hadoop
Oracle Big Data SQL• Transparent access from Oracle
DB to Hadoop– Oracle SQL dialect– Oracle DB security model– Join data from Hadoop and Oracle
• SmartScan - pushing code to data– Same software base as on Exadata
Storage Cells– Minimize data transfer from Hadoop
to Oracle• Requires BDA and Exadata• Licensed per Hadoop disk spindle
26
Big Data SQL Demo
Big Data SQL in Oracle tools• Transparent to any app• SQL Developer• ODI• OBIEE
Hadoop as Data Warehouse
Traditional Needs of Data Warehouses• Speed of thought end user analytics
experience– BI tools coupled with DW databases
• Scalable data platform– DW database
• Versatile and scalable data transformation engine– ETL tools sometimes coupled with DW
databases• Data quality control and audit
– ETL tools
What drives Hadoop adoption for Data Warehousing?
What drives Hadoop adoption for Data Warehousing?
1. Cost efficiency
What drives Hadoop adoption for Data Warehousing?
1. Cost efficiency2. Agility needs
Why is Hadoop Cost Efficient?Hadoop leverages two main trends in IT industry
• Commodity hardware – high performance and high capacity at low price is available
• Commodity software – open-source phenomena made advanced software products affordable to anyone
How Does Hadoop Enable Agility?• Load first, structure later
– Don’t need to spend months changing DW to add new types of data without knowing for sure it will be valuable for end users
– Quick and easy to verify hypothesis – perfect data exploration platform
• All data in one place is very powerful– Much easier to test new theories
• Natural fit for “unstructured” data
Traditional needs of DW & Hadoop• Speed of thought end user analytics experience?
– Very recent features – Impala, Presto, Drill, Hadapt, etc.– BI tools embracing Hadoop as DW– Totally new products become available
• Scalable data platform?– Yes
• Versatile and scalable data transformation engine?– Yes but needs a lot of DIY– ETL vendors embraced Hadoop
• Data quality control and audit?– Hadoop makes it more difficult because of flexibility it brings– A lot of DIY but ETL vendors getting better supporting
Hadoop + new products appear
Getting there
Challenge
Unique Hadoop Challenges• Still “young” technology
– requires a lot of high quality engineering talent• Security doesn’t come out of the box
– Capabilities are there but very tedious to implement and somewhat fragile
• Challenge of selecting the right tool for the job– Hadoop ecosystem is huge
• Hadoop breaks IT silos• Requires commoditization of IT operations
– Large footprint with agile deployments
Typical Hadoop adoption in modern Enterprise IT
Data WarehouseHadoop
BI tools
Bring the world in your data center
Rare historical report
Find a needle in a haystack
Will Hadoop displace traditional DW platforms?
Hadoop
BI tools
Example pure Hadoop DW stack
HDFS
Hive/Pig FlumeSqoop DIYImpala
Ker
bero
s
Oozie + DIY -
data sources
Do you have a Big Data problem?
Your Datais NOTas BIG as you think
is NOT a Big Data problem
Using 8 years old hardware…
is NOT a Big Data problem
Misconfigured infrastructure…
is NOT a Big Data problem
Lack of purging policy…
is NOT a Big Data problem
Bad data model design…
is NOT a Big Data problem
Bad SQL…
Your Datais NOTas BIG as you think
Controversy…
Thanks and Q&AContact info
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