Hello folks, IOblend here. Hope you are all keeping well.
There is one thing that has been bugging us recently, which led to the writing of this blog. While working on several data projects with some of our clients, we observed instances when data lineage had not been implemented as part of the solutions. In a couple of cases, data lineage was entirely overlooked, which raised our eyebrows.
Data lineage is paramount from the data auditing point of view. How else would you keep track of what is happening to your data throughout its lifecycle? What if your systems go down and the data becomes corrupted? How would you know what data generated spurious results down the line? You will really struggle to restore your data to the correct state if you do not know where the problem is.
The common reason for data lineage omission was the time pressure to deploy a new system. Delivering the system was considered a much higher priority than ensuring the data quality that fed it. We get it, designing and scripting data lineage across your entire dataflows and data estate can be a massive undertaking, especially under time and resource pressure.
However, data issues always come to bite you in the long run. Just from the security and reliability points of view, you absolutely must be on top of your data happenings. Data lineage gives you that ability. The more granular data lineage is, the easier your life will be when things go wrong with your data.
Inevitably, you will have to implement data lineage, but then someone will have to code it from scratch. Data lineage must go all the way across the data from the source to the end point and cover the data at the lowest level regardless of the types. It should be the same granularity for all stakeholders, so everyone works off the base baseline. You will then have a much greater confidence in your data estate.
Implementing data lineage is not a simple job. You need to set and build in data quality and monitoring policies for all dataflows. Depending on your resources, this can be a daunting task. It is much trickier to implement if you are doing live data streaming. There are some tools available on the market that can help you with the task, but you need to make sure they can work well with the rest of your data estate and give you sufficient granularity.
Since we have encountered data lineage issues on more than one occasion, we made data lineage an integral part of our solution. We do DataOps, and data lineage is DataOps. At IOblend, we made sure that the most granular data lineage is available to you ‘out-of-the-box’. It starts at record level with the raw data and maps the transformations all the way to the end target. Our process utilises the power of Apache Spark™ but requires no coding whatsoever on the user’s part. Just visually design your dataflow and data lineage is applied automatically, every time.
Once applied, you can trace data lineage via IOblend or any other analytical tool you may use at your data end points. No hassle. Your data citizens will always have the full confidence in the quality of their data.
IOblend – make you data estate state-of-the-art
Stay safe and catch you soon
Data lineage is a “must have”, not “nice to have”
Hello folks, IOblend here. Hope you are all keeping well. There is one thing that has been bugging us recently, which led to the writing of this blog. While working on several data projects with some of our clients, we observed instances when data lineage had not been implemented as part of the solutions. In
Welcome to the IOblend blog
Welcome to the IOblend blog page. We are the creators of the IOblend real-time data integration and advanced DataOps solution. Over the many (many!) years, we have gained experience and insight from the world of data, especially in the data engineering and data management areas. Data challenges are everywhere and happen daily. We are sure,
