When we talk about big data, it can be difficult for the human mind to actually fathom the sheer volume data that is generated on a daily basis. I mean, I really don’t believe that there’s a word in the English language that can accurately describe how much data is out there.
No matter what industry you’re in, there’s going to be whole lot of data (both structured and unstructured) out there to work with. How well your business does in the future will depend on how this data is accessed, processed, and analyzed.
Further, this massive volume of data also creates a few problems such as integration, storage, and accessibility.
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So what’s the solution?
At present, the key to overcoming these obstacles is Hadoop architecture based data lakes. It essentially enables the use of parallel commodity hardware and open software standards to process and distribute big data. Further, Hadoop can be highly cost-effective as it’s approximately 10 to 100 times cheaper to deploy (so more often than not, Hadoop is used to deal with data lakes).
What are Data Lakes?
Data lakes are essentially repositories for huge varieties and quantities of data (both structured and unstructured). For example, records can be stored in their unstructured native formats for analyzing at a later date. Most often, the data will be generated by sensors from machines and smart devices.
Data warehouses or data marts fall short as they require the data to be structured and integrated right from the beginning. Data warehouses come with a built in schema of data and as a result you’re stuck with it.
Data lakes on the hand store raw data in the form they came in, so you can choose the schema and use the data to make sense of the data for your own objectives.
Further, data attribution and fidelity can be maintained easily by preserving the native format. This in turn will enable you to perform various analyses using different contexts. With data lakes, we can now run several data analysis projects enabling various measurements and predictions. As a result, enterprise data lakes will continue to grow in importance for business intelligence (BI) making it a vital component in the decision making process.
So How Can Data Lakes Enhance BI?
BI can be better with enterprise data lakes as it can help resolve the problem with integration and accessibility. Further, as it’s cost-effective, even small businesses can take advantage of big data.
Data lakes essentially create unlimited potential by enabling variations in modeling and relaxing standardization. This is a departure from previous approaches that were broad-based data integration models limited to a predetermined schema.
With enterprise data lakes, businesses big and small have a great opportunity to engage in data discovery and operational insight. Companies are also able to collectively collaborate and create views or models of data and incrementally manage and improve metadata.
Tools used by business analysts and data scientists are as follows:
- Revelytix Loom
- Apache Falcon
Hadoop also enables linage tracking of metadata with Hadoop Distributed File System (HDFS). With HDFS, various chunks of data are distributed over a cluster of servers in a cloud. As a result, data analytics becomes vital to BI as metadata can be viewed from different contexts as the data flows in.
With access to data lakes, BI can more or less have a 360-degree view of social media trends and target markets. This in turn should bring about an end to data silos.
If the information stored in enterprise data lakes are harnessed effectively, business will be able to make accurate predictions and get the highest ROI. However, it’s still early days and it will be interesting to see how data lakes evolve across all industries.
Some initiatives have been unsuccessful and ended up creating more silos, so we need to proceed with caution. Businesses need to understand that you can’t just dump all the data into HDFS and hope for the best. There needs to be efficient data management, security, and more tools need to be developed to get the maximum value from the stored data.
So far, the challenge isn’t building data lakes, its actually taking advantage of them.