Important Steps To Keep Your Data Lake From Turning Into A Data Swamp
As the repository of data expands with each passing day, deriving meaningful insights from it through analytics has become a challenge. With organizations feeling the need to move this data around for better understanding and collaboration between different departments, data lakes have emerged. In simple terms, these are a large body of raw data where users examine data and extract information from it. However, to use it efficiently, one needs better data governance and management. This is because, without that, it leads to something called ‘data swamps’. While there are many steps one can take to ensure that data lakes do not turn into swamps, we have attempted to explain some of the essential ones: Start With Project-Specific Data One of the biggest reasons for data lakes to fail is the lack of planning. Organizations dump all company-related data into their data lakes – this should not be done. Instead, they should build their data lake based on various projects. While the point of having a data lake is to have all company-related information in one place, the answer is to not turn it into a swamp by striking the right balance. Company has to find a balance between the quantity of its analytics data, and the value its data lake holds for its business functions. Catalog The Data On Ingest Cataloguing the data on ingest makes it more searchable. This is something which is done when the data is brought into the lake. The organization should make the data easy to find for its proper analysis. Cataloguing also helps in eliminating accidental loading of the same data source more than once. This step requires immediate attention. Loading the data into the lake and leaving it to catalogue in the future is a big mistake. This is because cataloguing the data from the data lake after some time has passed will prove to be difficult and time-consuming. Loading Data Only Once Loading data poses two challenges – the first is managing the large data file systems. These large data file systems require loading one entire file at a time. When it comes to loading small tables and files, it is not difficult, but as the file size increases, loading these can become a problem as it will take more time. One can minimise the time it takes to load large source data sets by loading the entire data set once, and later merging and syncing the changes in the data lake. Another challenge could be when two different people try to load the same data source onto different parts of the data lake. The DBAs responsible for upstream data sources getting loaded into the data lake will face problems, as the lake will consume too much capacity to load the data. This will result in the data lake interrupting in operational databases that are used for businesses. Documenting Data Lineage & Good Governance Implementation Once the data lake is in use, different people who use it might clean it or start integrating it with other data sets. And while people do this, often when someone wants to implement a project they are interested in, chances are the data related to it might have already been cleaned. Now, when one is only familiar with the raw data of the project they are interested in and not any other version, they will have to redo the work that has already been done. To avoid this problem, documenting changes related to the data thoroughly and implementing solid governance processes that bring to light the interactions people have had to ingest and transform data is important.
As the repository of data expands with each passing day, deriving meaningful insights from it through analytics has become a challenge. With organizations feeling the need to move this data around for better understanding and collaboration between different departments, data lakes have emerged. In simple terms, these are a large body of raw data where […]
To use data lakes efficiently, one needs better data governance and management, without these they turn into ‘data swamps’
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["AI", "analytics", "R", "Go", "data lake", "data governance", "data lineage", "GAN", "programming_languages:R", "programming_languages:Go"]
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