Is it time to bid adieu to the traditional data warehouse and embrace a new generation tool or may be we need to bridge the gap between different types and sources of data. Let us explore an ETL tool with a series of tutorials to understand the problem and find out a possible solution.
As someone who is working in the data warehousing field, I have been often baffled about the complexity of choosing the right ETL tool for doing the job. This problem of choosing the right tool led me to a series of problems about the deployment complexity of ETL tools and the collection of data. A year ago I was carrying out my own analysis on the performance of students whom I have trained in database and data warehousing technologies and I came across an interesting situation. The data required for this analysis were mostly in flat files or excel sheets or an RDBMS. At the same time, around 10 percent of the data were sent through mails but if I have to carry out an impartial analysis then I cannot exclude that part from my analysis. At the same time, some amount of data was in a NoSQL database and this is where the problem started.I have to set up such a system where I will have data integrated from all these sources.The problem now was which ETL tool should I choose which will involve less complexity in terms of extraction of data and deployment and obviously should be easy to learn.
Informatica power center and SSIS have been my hot favourites for reasons of their own but what is this open-source tool Talend?
The greatest challenge in the field of data integration revolves around flexibility. The modern requirement is obviously the power to extract data from as many numbers of sources as possible and this data may be in the cloud or on the ground and while choosing the ETL tool this has to be the deciding factor.
Is Talend easier to use?
As compared to other ETL tools, Talend does have a low learning curve that can combine and convert data easily.The easy to use interfaces gives easy access to the metadata repository from where we can reuse our work. One of the most powerful features of Talend is the ability to extract information from big data. It also plays a significant role in cutting down on the cost with its ability to build, extract data elements apart from the fact that being an open-source tool it has got its own advantages.
Limitations of traditional ETL tools.
All the traditional ETL tools were designed for carrying out batch loads and it is quite a useful feature but whenever we have data from multiple sources this batch load becomes a daunting task especially when change data capture is involved. This proves quite a challenge for the ETL developer as the modern world generates an enormous amount of data that has to be refreshed at an equal pace.
Modern ETL tool requirement
The modern ETL tool requires to be integrated well with cloud-based services and streaming has to be done with the cloud in mind.The modern ETL tool has to bridge the gap between the traditional loading process and has to offer support or cloud and merge data from all types of sources. The ETL process has to be taken to the next level where all these features can be incorporated.
Talend and big data.
Talend has an environment where it can interact with big data sources without the complication of intricate coding. Talend Big Data Platform simplifies complex integrations to take advantage of Apache Spark, AWS and NoSQL and provides integrated data quality so we can turn big data into capital.
I will be publishing a series of tutorials on Talend based on my personal experiences which I am sure will be beneficial to someone who is new to the world of modern data warehouses as well as an intermediate level ETL developer who has to go through this crucial task of choosing the right tool to unleash the power of data.