Data Lake enables teams to mine insights out of voluminous amounts of semi-structured and unstructured data. An enterprise can leverage this data to do business transactions more efficiently than their competitors. Data lake models have yielded notable successes for many enterprises but there are also others who are scrambling to get it right.
IDC study heralds that world data will grow by 163 zettabytes (ZB) by 2025 and there will be transformations and obstacles before enterprises in processing this data into a data lake. Hard-coded algorithms and manual processing will lack the cadence to garner efficiencies. Committed leadership, appropriate communications, end-to-end integration are some of the factors necessary for its success.
Enterprises rushing to implement Data Lake need to build the foundation for its success. Answering these 5 questions can help you ensure that your data lake project is headed in the right direction.
1. What is your overarching business strategy?
'Everyone has a data lake' is not the best reason to kick-start a data lake project. You need to understand why you need to go through it and what is the best approach to achieve goals. How the data is flowing through between source and target systems. It can be tiresome to move data between siloed systems which can't be scaled effectively. You need to define the goals and place the right triggers to support it.
2. What is your data mix?
Not all data models are created equal, so it is important to consider the data mix as well. If you don't want differentiated data models to water down data lake competencies than you should closely evaluate your software stack and data models. Consider real-time data streaming capabilities if the data is confluencing at multiple sources. The ability to handle volumes of data smoothens the path to conversion and analysis.
3. Have you found the right skills to execute a Data Lake?
Your data lake project will require the right expert to get the job done and done well. Data warehouse experts, business analysts, etc. who are not versed with Big Data universe cannot deliver results. Your team should have experience in coordinating a portfolio of initiatives with Hadoop, Flink, Kafka, or Spark. Apart from this, your project will require data scientists who can leverage programming languages like Python or R for data preparation.
4. What about Large File Data Processing capability?
Sequential delays in processing data populated in the warehouse can negatively impact the outcome. Make sure you have the large file data integration capability to process colossal volumes of unstructured data struck in various sources. Large file data ingestion capability helps in preparing data before it gets stored and simplifies reporting, analysis, and business monitoring.
5. What will be your data integration strategy?
A lot of operational aspects in terms of data integration need to be changed for data lake projects. Functional silos are the reason at the first place that enterprises are unable to find gold with their data lake. Ensure that your organization has a realistic data integration strategy for moving and storing data. A good data integration strategy enables cross-functional teams to monitor and govern data in a better way.