Wednesday Aug 05, 2015

Chalk Talk Video: Oracle Big Data Preparation Cloud Service

We continue our Oracle Data Integration chalk talk video series, with an overview of Oracle Big Data Preparation Cloud Service (BDP). BDP allows users to unlock the potential of their data with a non-technical, web-based tool that minimizes data preparation time. BDP provides an interactive set of services that automate, streamline, and guide the process of data ingestion, preparation, enrichment, and governance without costly manual intervention.

View this video to learn more: Chalk Talk: Oracle Big Data Preparation Cloud Service

For additional information – visit the Oracle Big Data Preparation Cloud Service page.


Tuesday Jul 07, 2015

Chalk Talk Video: Kick-Start Big Data Integration with Oracle

Next in the series for Oracle Data Integration chalk talk videos, we speak to Oracle Data Integrator (ODI) for big data. ODI allows you to become a big data developer without learning to code Java and Map Reduce! ODI generates the code and optimizes it with support for Hive, Spark, Oozie, and Pig.

View this video to learn more: Chalk Talk: Kick-Start Big Data Integration with Oracle.

For additional information on Oracle Data Integrator, visit the ODI homepage and the ODI for Big Data page. This blog can be very handy also: Announcing Oracle Data Integrator for Big Data.

Thursday Jul 02, 2015

Chalk Talk Video: How to Raise Trust and Transparency in Big Data with Oracle Metadata Management

Some fun new videos are available; we call the series ‘Chalk Talk’!

The first in the series that we will share with you around Oracle Data Integration speaks to raising trust and transparency within big data. It is known that crucial big data projects often fail due to a lack in the overall trust of the data. Data is not always transparent, and governing it can become a costly overhead. Oracle Metadata Management assists in the governance and trust across all data with the enterprise, Oracle and 3rd party.

View this video to learn more: Chalk Talk: How to Raise Trust and Transparency in Big Data.

For additional information on Oracle Metadata Management, visit the OEMM homepage.

Wednesday May 13, 2015

Looking for Cutting-Edge Data Integration: 2015 Excellence Awards

It is nomination time!!!

This year's Oracle Fusion Middleware Excellence Awards will honor customers and partners who are creatively using various products across Oracle Fusion Middleware. Think you have something unique and innovative with Oracle Data Integration products?

We'd love to hear from you! Please submit today in the Big Data and Analytics category.

The deadline for the nomination is July 31, 2015. Win a free pass to Oracle OpenWorld 2015!!

Let’s reminisce a little…

For details on the 2014 Data Integration Winners: NET Serviços and Griffith University, check out this blog post.

For details on the 2013 Data Integration Winners: Royal Bank of Scotland’s Market and International Banking and The Yalumba Wine Company, check out this blog post.

For details on the 2012 Data Integration Winners: Raymond James Financial and Morrisons Supermarkets, check out this blog post.

We hope to honor you!

Click here to submit your nomination today. And just a reminder: the deadline to submit a nomination is 5pm Pacific Time on July 31, 2015.

Monday May 11, 2015

Oracle Big Data Preparation Cloud Service (BDP) – Coming Soon

What are your plans around Big Data and Cloud?

If your organization has already begun to explore these topics, you might be interested a new offering from Oracle that will dramatically simplify how you use your data in Hadoop and the Cloud:

Oracle Big Data Preparation Cloud Service (BDP)

There is a perception that most of the time spent in Big Data projects is dedicated to harvesting value. The reality is that 90% of the time in Big Data projects is really spent on data preparation. Data may be structured, but more often it will be semi-structured such as weblogs, or fully unstructured such as free form text. The content is vast, inconsistent, and incomplete, often off topic, and from multiple differing formats and sources. In this environment each new dataset takes weeks or months of effort to process, frequently requiring programmers writing custom scripts. Minimizing data preparation time is the key to unlocking the potential of Big Data.

Oracle Big Data Preparation Cloud Service (BDP) addresses this very reality. BDP is a non-technical, web-based tool that sets out to minimize data preparation time in an effort to quickly unlock the potential of your data. The BDP tool provides an interactive set of services that automate, streamline, and guide the process of data ingestion, preparation, enrichment, and governance without costly manual intervention.

The technology behind this service is amazing; it intuitively guides the user with a machine learning driven recommendation engine based on semantic data classification and natural language processing algorithms. But the best part is that non-technical staff can use this tool as easily as they use Excel, resulting in a significant cost advantage for data intensive projects by reducing the amount of time and resources required to ingest and prepare new datasets for downstream IT processes.

Curious to find out more? We invite you to view a short demonstration of BDP below:

Let us know what you think!

Stay tuned as we write more about this offering… visit often here!

Friday Apr 10, 2015

Customers Tell All: What Sets Oracle Apart in Big Data Integration

Data integration has become a critical component of many technology solutions that businesses pursue to differentiate in their markets. Instead of relying on manual coding in house, more and more businesses choose data integration solutions to support their strategic IT initiatives, from big data analytics to cloud integration.

To explore the differences among the leading data integration solutions and the impact their technologies are having on real-world businesses, Dao Research recently conducted a research study, where they interviewed IBM, Informatica, and Oracle customers. In addition they reviewed publicly available solution information from these three vendors.

The research revealed some key findings that explains Oracle's leadership in the data integration space. For example:

  • Customers who participated in this study cite a range of 30 to 60 % greater development productivity using Oracle Data Integrator vs traditional ETL tools from Informatica and IBM. Dao's research ties Oracle's advantage to product architecture differences such as native push-down processing, the seperation of logical and physical layers, and the ability to extend Oracle Data Integrator using its knowledge modules.
  • The research also showed that Oracle’s data integration cost of ownership is lower because of its unified platform strategy (versus offering multiple platforms and options), its use of source and target databases for processing, higher developer productivity, faster implementation, and it doesn’t require management resources for a middle-tier integration infrastructure.
  • In the area of big data integration, the study highlights Oracle’s advantage with its flexible and native solutions. Unlike competitors’ offerings, developed as separate solutions, Oracle’s solution is aware of the cluster environment of big data systems. Oracle enables big data integration and cloud data integration through the use of a single platform with common tooling and inherent support for big data processing environments.
  • I should add that the latest release of Oracle Data Integrator EE Big Data Options  widens the competitive gap. Oracle is the only vendor that can automatically generate Spark, Hive, and Pig transformations from a single mapping. Oracle Data Integration customers can focus on building the right architecture for driving business value, and do not have to become expert on multiple programming languages.  For example, an integration architect in a large financial services provider told the research company "As an ODI developer, I am a Big Data developer without having to understand the underpinnings of Big Data. That's pretty powerful capability."


You can find the report of Dao's research here:

I invite you to read this research paper to understand why more and more customers trust Oracle for their strategic data integration initiatives after working with or evaluating competitive offerings.


Thursday Apr 09, 2015

ODI, Big Data SQL and Oracle NoSQL

Back in January Anuj posted an article here on using Oracle NoSQL via the Oracle database Big Data SQL feature. In this post, I guess you could call it part 2 of Anuj's I will follow up with how the Oracle external table is configured and how it all hangs together with manual code and via ODI. For this I used the Big Data Lite VM and also the newly released Oracle Data Integrator Big Data option. The BDA Lite VM 4.1 release uses version 3.2.5 of Oracle NoSQL - from this release I used the new declarative DDL for Oracle NoSQL to project the shape from NoSQL with some help from Anuj.

My goal for the integration design is to show a logical design in ODI and how KMs are used to realize the implementation and leverage Oracle Big Data SQL - this integration design supports predicate pushdown so I actually minimize data moved from my NoSQL store on Hadoop and the Oracle database - think speed and scalability! My NoSQL store contains user movie recommendations. I want to join this with reference data in Oracle which includes the customer information, movie and genre information and store in a summary table.

Here is the code to create and load the recommendation data in NoSQL - this would normally be computed by another piece of application logic in a real world scenario;

  • export KVHOME=/u01/nosql/kv-3.2.5
  • cd /u01/nosql/scripts
  • ./admin.sh

  • connect store -name kvstore
  • EXEC "CREATE TABLE recommendation( \
  •          custid INTEGER, \
  •          sno INTEGER, \
  •          genreid INTEGER,\
  •          movieid INTEGER,\
  •          PRIMARY KEY (SHARD(custid), sno, genreid, movieid))"
  • PUT TABLE -name RECOMMENDATION  -file /home/oracle/movie/moviework/bigdatasql/nosqldb/user_movie.json

The Manual Approach

This example is using the new data definition language in NoSQL. To make this accessible via Hive, users can create Hive external tables that use the NoSQL Storage Handler provided by Oracle. If this were manually coded in Hive, we could define the table as follows;

  • CREATE EXTERNAL TABLE IF NOT EXISTS recommendation(
  •                  custid INT,
  •                  sno INT,
  •                  genreId INT,
  •                  movieId INT)
  •           STORED BY 'oracle.kv.hadoop.hive.table.TableStorageHandler'
  •           TBLPROPERTIES  ( "oracle.kv.kvstore"="kvstore",
  •                            "oracle.kv.hosts"="localhost:5000",
  •                            "oracle.kv.hadoop.hosts"="localhost",
  •                            "oracle.kv.tableName"="recommendation");

At this point we have made NoSQL accessible to many components in the Hadoop stack - pretty much every component in the hadoop ecosystem can leverage the HCatalog entries defined be they Hive, Pig, Spark and so on. We are looking at Oracle Big Data SQL tho, so let's see how that is achieved. We must define an external table that uses either the SerDe or a Hive table, below you can see how the table has been defined in Oracle;

  • CREATE TABLE recommendation(
  •                  custid NUMBER,
  •                  sno NUMBER,
  •                  genreid NUMBER,
  •                  movieid NUMBER
  •          )
  •                  ORGANIZATION EXTERNAL
  •          (
  •                  TYPE ORACLE_HIVE
  •                  DEFAULT DIRECTORY DEFAULT_DIR
  •                  ACCESS PARAMETERS  (
  •                      com.oracle.bigdata.tablename=default.recommendation
  •                  )
  •          ) ;

Now we are ready to write SQL! Really!? Well let's see, below we can see the type of query we can do to join the NoSQL data with our Oracle reference data;

  • SELECT m.title, g.name, c.first_name
  • FROM recommendation r, movie m, genre g, customer c
  • WHERE r.movieid=m.movie_id and r.genreid=g.genre_id and r.custid=c.cust_id and r.custid=1255601 and r.sno=1 
  • ORDER by r.sno, r.genreid;

Great, we can now access the data from Oracle - we benefit from the scalability of the solution and minimal data movement! Let's make it better, let's make it more maintainable, flexibility to future changes and also accessible by more people by showing how it is done in ODI.

Oracle Data Integrator Approach

The data in NoSQL has a shape, we can capture that shape in ODI just as it is defined in NoSQL. We can then design mappings that manipulate the shape and load into whatever target we like. The SQL we saw above is represented in a logical mapping as below;


Users can use the same design experience as other data items and benefit from the mapping designer. They can join, map, transform just as normal. The ODI designer allows you to separate how you physically want this to happen from the logical semantics - this is all about giving you flexibility to change and adapt to new integration technologies and patterns.

In the physical design we can assign Knowledge Modules that take the responsibility of building the integration objects that we previously manually coded above. These KMs are generic so support all shapes and sizes of data items. Below you can see how the LKM is assigned for accessing Hive from Oracle;

This KM takes the role of building the external table - you can take this use it, customize it and the logical design stays the same. Why is that important? Integration recipes CHANGE as we learn more and developers build newer and better mechanisms to integrate. 

This KM takes care of creating the external table in Hive that access our NoSQL system. You could also have manually built the external table and imported this into ODI and used that as a source for the mapping, I want to show how the raw items can be integrated as the more metadata we have and you use to design the greater the flexibility in the future. The LKM Oracle NoSQL to Hive uses regular KM APIs to build the access object, here is a snippet from the KM;

  • create table <%=odiRef.getObjectName("L", odiRef.getTableName("COLL_SHORT_NAME"), "W")%>
  •  <%=odiRef.getColList("(", "[COL_NAME] [DEST_CRE_DT]", ", ", ")", "")%> 
  •           STORED BY 'oracle.kv.hadoop.hive.table.TableStorageHandler'
  •           TBLPROPERTIES  ( "oracle.kv.kvstore"="<%=odiRef.getInfo("SRC_SCHEMA")%>",
  •                            "oracle.kv.hosts"="<%=odiRef.getInfo("SRC_DSERV_NAME")%>",
  •                            "oracle.kv.hadoop.hosts"="localhost",
  •                            "oracle.kv.tableName"="<%=odiRef.getSrcTablesList("", "[TABLE_NAME]", ", ", "")%>");

You can see the templatized code versus literals, this still needs some work as you can see, can you spot some hard-wiring that needs fixed? ;-) This was using the 12.1.3.0.1 Big Data option of ODI so integration with Hive is much improved and it leverages the DataDirect driver which is also a big improvement. In this post I created a new technology for Oracle NoSQL in ODI, you can do this too for anything you want, I will post this technology on java.net and more so that as a community we can learn and share.

Summary 

Here we have seen how we can make seemingly complex integration tasks quite simple and leverage the best of data integration technologies today and importantly in the future!


Monday Apr 06, 2015

Announcing Oracle Data Integrator for Big Data

Proudly announcing the availability of Oracle Data Integrator for Big Data. This release is the latest in the series of advanced Big Data updates and features that Oracle Data Integration is rolling out for customers to help take their Hadoop projects to the next level.

Increasing Big Data Heterogeneity and Transparency

This release sees significant additions in heterogeneity and governance for customers. Some significant highlights of this release include

  • Support for Apache Spark,
  • Support for Apache Pig, and
  • Orchestration using Oozie.

Click here for a detailed list of what is new in Oracle Data Integrator (ODI).

Oracle Data Integrator for Big Data helps transform and enrich data within the big data reservoir/data lake without users having to learn the languages necessary to manipulate them. ODI for Big Data generates native code that is then run on the underlying Hadoop platform without requiring any additional agents. ODI separates the design interface to build logic and the physical implementation layer to run the code. This allows ODI users to build business and data mappings without having to learn HiveQL, Pig Latin and Map Reduce.

Oracle Data Integrator for Big Data Webcast

We invite you to join us on the 30th of April for our webcast to learn more about Oracle Data Integrator for Big data and to get your questions answered about Big Data Integration. We discuss how the newly announced Oracle Data Integrator for Big Data

  • Provides advanced scale and expanded heterogeneity for big data projects 
  • Uniquely compliments Hadoop’s strengths to accelerate decision making, and 
  • Ensures sub second latency with Oracle GoldenGate for Big Data.


Thursday Mar 26, 2015

Oracle Big Data Lite 4.1.0 is available with more on Oracle GoldenGate and Oracle Data Integrator

Oracle's big data team has announced the newest Oracle Big Data Lite Virtual Machine 4.1.0.  This newest Big Data Lite Virtual Machine contains great improvements from a data integration perspective with inclusion of the recently released Oracle GoldenGate for Big Data.  You will see this in an improved demonstration that highlights inserts, updates, and deletes into Hive using Oracle GoldenGate for Big Data with Oracle Data Integrator performing a merge of the new operations into a consolidated table.

Big Data Lite is a pre-built environment which includes many of the key capabilities for Oracle's big data platform.   The components have been configured to work together in this Virtual Machine, providing a simple way to get started in a big data environment.  The components include Oracle Database, Cloudera Distribution including Apache Hadoop, Oracle Data Integrator, Oracle GoldenGate amongst others. 

Big Data Lite also contains hands-on labs and demonstrations to help you get started using the system.  Tame Big Data with Oracle Data Integration is a hands-on lab that teaches you how to design Hadoop data integration using Oracle Data Integrator and Oracle GoldenGate. 

                Start here to learn more!  Enjoy!

Thursday Feb 19, 2015

Introducing Oracle GoldenGate for Big Data!

Big data systems and big data analytics solutions are becoming critical components of modern information management architectures.  Organizations realize that by combining structured transactional data with semi-structured and unstructured data they can realize the full potential value of their data assets, and achieve enhanced business insight. Businesses also notice that in today’s fast-paced, digital business environment to be agile and respond with immediacy, access to data with low latency is essential. Low-latency transactional data brings additional value especially for dynamically changing operations that day-old data, structured or unstructured, cannot deliver.

Today we announced the general availability of Oracle GoldenGate for Big Data product, which offers a real-time transactional data streaming platform into big data systems. By providing easy-to-use, real-time data integration for big data systems, Oracle GoldenGate for Big Data facilitates improved business insight for better customer experience. It also allows IT organizations to quickly move ahead with their big data projects without extensive training and management resources. Oracle GoldenGate for Big Data's real-time data streaming platform also allows customers to keep their big data reservoirs up to date with their production systems. 

Oracle GoldenGate’s fault-tolerant, secure and flexible architecture shines in this new big data streaming offering as well. Customers can enjoy secure and reliable data streaming with subseconds latency. With Oracle GoldenGate’s core log-based change data capture capabilities it enables real-time streaming without degrading the performance of the source production systems.

The new offering, Oracle GoldenGate for Big Data, provides integration for Apache Flume, Apache HDFS, Apache Hive and Apache Hbase. It also includes Oracle GoldenGate for Java, which enables customers to easily integrate to additional big data systems, such as Oracle NoSQL, Apache Kafka, Apache Storm, Apache Spark, and others.

You can learn more about our new offering via Oracle GoldenGate for Big Data data sheet and by registering for our upcoming webcast:

How to Future-Proof your Big Data Integration Solution

March 5th, 2015 10am PT/ 1pm ET

I invite you to join this webcast to learn from Oracle and Cloudera executives how to future-proof your big data infrastructure. The webcast will discuss :

  • Selection criteria that will drive business results with Big Data Integration 
  • Oracle's new big data integration and governance offerings, including Oracle GoldenGate for Big Data
  • Oracle’s comprehensive big data features in a unified platform 
  • How Cloudera Enterprise Data Hub and Oracle Data Integration combine to offer complementary features to store data in full fidelity, to transform and enrich the data for increased business efficiency and insights.

Hope you can join us and ask your questions to the experts.

Thursday Jan 22, 2015

OTN Virtual Technology Summit Data Integration Subtrack Features Big Data Integration and Governance

I am sure many of you have heard about the quarterly Oracle Technology Network (OTN) Virtual Technology Summits. It provides a hands-on learning experience on the latest offerings from Oracle by bringing experts from our community and product management team. 

The next OTN Virtual Technology Summit is scheduled to February 11th (9am-12:30pm PT) and will feature Oracle's big data integration and metadata management capabilities with hands-on-lab content.

The Data Integration and Data Warehousing sub-track includes the following sessions and speakers:

Feb 11th 9:30am PT -- HOL: Real-Time Data Replication to Hadoop using GoldenGate 12c Adaptors

Oracle GoldenGate 12c is well known for its highly performant data replication between relational databases. With the GoldenGate Adaptors, the tool can now apply the source transactions to a Big Data target, such as HDFS. In this session, we'll explore the different options for utilizing Oracle GoldenGate 12c to perform real-time data replication from a relational source database into HDFS. The GoldenGate Adaptors will be used to load movie data from the source to HDFS for use by Hive. Next, we'll take the demo a step further and publish the source transactions to a Flume agent, allowing Flume to handle the final load into the targets.

Speaker: Michael Rainey, Oracle ACE, Principal Consultant, Rittman Mead

Feb 11th 10:30am PT -- HOL: Bringing Oracle Big Data SQL to Oracle Data Integration 12c Mappings

Oracle Big Data SQL extends Oracle SQL and Oracle Exadata SmartScan technology to Hadoop, giving developers the ability to execute Oracle SQL transformations against Apache Hive tables and extending the Oracle Database data dictionary to the Hive metastore. In this session we'll look at how Oracle Big Data SQL can be used to create ODI12c mappings against both Oracle Database and Hive tables, to combine customer data held in Oracle tables with incoming purchase activities stored on a Hadoop cluster. We'll look at the new transformation capabilities this gives you over Hadoop data, and how you can use ODI12c's Sqoop integration to copy the combined dataset back into the Hadoop environment.

Speaker: Mark Rittman, Oracle ACE Director, CTO and Co-Founder, Rittman Mead

Feb 11th 11:30am PT-- An Introduction to Oracle Enterprise Metadata Manager
This session takes a deep technical dive into the recently released Oracle Enterprise Metadata Manager. You’ll see the standard features of data lineage, impact analysis and version management applied across a myriad of Oracle and non-Oracle technologies into a consistent metadata whole, including Oracle Database, Oracle Data Integrator, Oracle Business Intelligence and Hadoop. This session will examine the Oracle Enterprise Metadata Manager "bridge" architecture and how it is similar to the ODI knowledge module. You will learn how to harvest individual sources of metadata, such as OBIEE, ODI, the Oracle Database and Hadoop, and you will learn how to create OEMM configurations that contain multiple metadata stores as a single coherent metadata strategy.

Speaker: Stewart Bryson, Oracle ACE Director, Owner and Co-founder, Red Pill Analytics

I invite you to register now to this free event and enjoy this feast for big data integration and governance enthusiasts.

Americas -- February 11th/ 9am to 12:30pm PT- Register Now

Please note the same OTN Virtual Technology Summit content will be presented again to EMEA and APAC. You can register for the via the links below.

EMEA – February 25th / 9am to 12:30pm GMT* - Register Now

APAC – March 4th / 9:30am-1:00pm IST* - Register Now

Join us and let us know how you like the data integration sessions in this quarter's OTN event.

Monday Dec 29, 2014

Oracle Data Enrichment Cloud Service (ODECS) - Coming Soon

What are your plans around Big Data and Cloud?

If your organization has already begun to explore these topics, you might be interested a new offering from Oracle that will dramatically simplify how you use your data in Hadoop and the Cloud:

Oracle Data Enrichment Cloud Service (ODECS)

There is a perception that most of the time spent in Big Data projects is dedicated to harvesting value. The reality is that 90% of the time in Big Data projects is really spent on data preparation. Data may be structured, but more often it will be semi-structured such as weblogs, or fully unstructured such as free form text. The content is vast, inconsistent, and incomplete, often off topic, and from multiple differing formats and sources. In this environment each new dataset takes weeks or months of effort to process, frequently requiring programmers writing custom scripts. Minimizing data preparation time is the key to unlocking the potential of Big Data.

Oracle Data Enrichment Cloud Service (ODECS) addresses this very reality. ODECS is a non-technical, web-based tool that sets out to minimize data preparation time in an effort to quickly unlock the potential of your data. The ODECS tool provides an interactive set of services that automate, streamline, and guide the process of data ingestion, preparation, enrichment, and governance without costly manual intervention.

The technology behind this service is amazing; it intuitively guides the user with a machine learning driven recommendation engine based on semantic data classification and natural language processing algorithms. But the best part is that non-technical staff can use this tool as easily as they use Excel, resulting in a significant cost advantage for data intensive projects by reducing the amount of time and resources required to ingest and prepare new datasets for downstream IT processes.

Curious to find out more? We invite you to view a short demonstration of ODECS below: