ETL Technology in market

It is time to start tool based posts with Datastage and sharing knowledge. 

Before going deeper on Datastage, it is better to define what is ETL and what is current understanding and position of this technology in the market, for the ones new to the topic. 

ETL ("extract, transform, load") is used to define process or supporting tools that are used to pull data out of one source (database, file ...etc), make necessary transformations and load to another database as a target. As I stated in my previous post "Why data integration geting more important?" , there are main requirements that makes us to think on data integration. Mainly,

(-) Data is not a stand alone asset anymore for enterprises or organizations. 

(+) Data is a commodity moving around the enterprise and going in to and coming out from other processes, other systems, other enterprises...etc. 

To handle this commodity throughout your systems, enterprise and even your whole environment; instead of just worrying about full-compatibility between systems, you need to consider about your data integration capability. Compatibility and bundling of complementary tools is used as a marketing&sales strategy in the develeopment era of information technologies or mainly in computer science. However with the explosion of knowledge and technological develoments, it is almost impossible for a vendor to respond and meet all expectations in the market. Probably you already watch out that vendors more strictly stick to standards and more focused expertise together with partnership approach is being more popular. 

With the discusions on Bigdata and NoSQL systems, there are two main ideas on whether ETL will be still in use or not. You can find viewpoints of Phil Shelley, former CTO Sears Holdings, CEO Metascale who has also established his Bigdata consulting firm NPP-Newton Park Partners last year(2013) and James Markarian, CTO Informatica  from different sides in InformationWeek article.

It is impossible not to listen Shelley's idea that "since Hadoop came to the enterprise, we are beginning to see the end of ETL as we know it". But there are points that I do not completely agree with Shelley. While he is saying ETL, he is just focusing the technology as it is now but we know that technology is tend to evolve acording to requirements in the market and it is not necessarily to be called as a new technology. Additionally, I do not see each stage of ETL as  non-value-added activities. Within the context of relational databases and structured data, with a good design and good performance, you can add value to your data and turn your stand-alone asset to a commodity that can be used for different purposes throughout your organisation with ETL. Shelley might be completely right for Hadoop but I really suspect whether "Hadoop came to enterprise"! Although it comes to enterprise, is it possible to have Hadoop as the only system? He also states "Some subsets of data do have to be moved out of Hadoop into other systems, for specific purposes. However, with a strong and coherent enterprise data architecture, this can be managed to be the exception." 

I do not want to make you lost in different articles before startig to learn a tool but I also strongly believe that it is better to understand the requirement and motivation behind any effort. ıt might also be good to have a look at "The State of ETL: Extract, Transform and Load Techology" article written by Alan R. Earls in DataInformed.

Not just for ETL but for all technologies, it is better to take it as a conceptual knowledge and make use of it to understand new technologies. It is more likely that ETL will not exist for too many years as its traditional form but the logic behind it to extract (filter and read, not necesarily to load to a different system) data, transform if necessary and to load (to a new system, to a modeling tool or just to a user interface) to make data available to serve specific purposes will retain. 

Hope you will find Datastage posts helpful for your ongoing tasks and to have a vision to get ready for new technologies. 

http://www-01.ibm.com/software/data/infosphere/

Why Data Integration getting more important?

The basic definition for data integration is "combining data from different sources and providing unified view of data to users to support decision making".

When we have a quick look why data integration is getting more important?

• Technology is taking place in all processes within an enterprise and this requires data integration from transactional and operational systems to decision support systems

• Everyday new technologies, which give new opportunities to enterprises, are being developed. This requires data integration between current systems and new systems

• Data is everywhere, in every type and produced by everyone. Unstructured data use is growing and importance of Big Data is increasing which also require new technologies in data integration field

• Data production and consumption cycle is getting shorter and real-time data requirements are increasing which also requires developments in the field

In AberdeenGroup's report, "Ever Harder and Faster: Managing the New Demands of Data Integration", we can see the increase in unstructured data use. I do not have any reference for more up-to-date data but I guess increase in external unstructured data use is higher. Thanks to developments in Big Data, it is possible now to analyze external data from internet and especially from social media to support decision making process. (e.g. to manage marketing campaign)

(http://v1.aberdeen.com/launch/report/perspective/8428-AI-business-data-integration.asp?lan=US)

In the same report, it is stated that "use of integration tools maximize the types and quantities of data that can be integrated and delivered to business managers". 

Data integration tools provide data quality and validation controls and make data transformation easy. If number of systems and data volume is small, coding might seem as an easy and applicable solution. However, when data requirements are increased, new data sources are added to the system, coding will get more complicated and performance will degrade. Data integration tools make it easy to expand data sources and to adopt new data  requirements. Other than these, tools provide other benefits like, real-time data process, tracebility (also by business users), automation ...etc.

There are different vendors providing tools or packages for data integration. At this point, it is good to have a look at Gartner's Magic Quadrant for Data Integration Tools.

Gartner's Magic Quadrant for Data Integration Tools

  (http://www.gartner.com/technology/reprints.do?id=1-1HBEFSF&ct=130717&st=sb)

You can find detailed strengths and cautions analysis of vendors in the report. Whether you are searching for a data integration tool or you are new to the field and trying to understand what should be expected from a data integration tool, this report will help you.