Data has evolved over the years. Data has been complex, unstructured, large in volume and in different forms like video, audio, text, jpeg, etc.

Real-time processing of these huge volumes of data and new types of data are all part of the evolution.  With this evolution of data, everyone wanted to use their databases to make better decisions. Technology giants then came up with data warehouses and business intelligence (BI) tools. Soon after, big data appeared, and people realized that the relational database and data warehouse would not be able to handle such huge volumes of data. There was a sudden shift from data warehouses to big data platforms. Many businesses turned to data lakes for the same reason.

Let us look at how Data lake is different from the data warehouse.

• Data lake is nothing but a repository to store data.
• It stores everything of big data and does not sort or classify the data, as the value of the data is not clear at the outset.
• As a result, data preparation is eliminated. A data lake is thus less organized compared to a data warehouse.
• Data lake classifies, organizes or analyzes the data only when the data is accessed. Data warehouses, on the other hand, aggregate structured data typically used to correlate broad business informational data to provide greater insights into corporate performance.

Since Business Intelligence tools (BI) did not work out with data warehouse, there was a shift to “big data BI”. Traditional BI tools supposedly support Hadoop, but they still require data to be extracted and transformed from Hadoop to a dedicated BI server.  The traditional BI platforms cannot efficiently handle big data. The use of BI tools for big data environment should rely on data lake clusters where applications and data reside. Data Lake provides an advanced environment for Business Intelligence reporting.

Undoubtedly, “in-data-lake BI” is the next big thing in BI. The benefits of using data lake BI reporting over traditional BI reporting are as follows:

1.    Distributed Architecture

As we know, traditional BI technologies, as well as modern platforms such as a Hadoop data lake cannot adequately handle large volumes of data.  That apart, dedicated BI servers and data warehouses that require scale-up growth or massively parallel architectures also mean higher costs for companies. Both techniques are far more expensive and limiting than a scale-out model.

The problem here is that, while the Hadoop-based data lake offers reliable storage and processing, the interaction with traditional BI tools presents a bottleneck for delivery of analytics to end users. Therefore, enterprises today are opting for an in-data-lake BI approach – it serves to be an appropriate option in terms of saving cost, time, and effort, and also provides the performance and user concurrency that is required in the processing of high-volume data.

2.    BI Process in Data Lake

Non-native BI tools require to extract databases which include a lot of downsides like redundancy and inconsistency with source data, data movement effort, extra systems to manage, and processing and storage overhead.

Extracting data also takes long which increase the risk that the data might be stale by the time the user sees it. On the other hand, in-data-lake BI can analyze the data as soon as it lands without the additional overhead of moving the data externally to a data mart or other dedicated BI platform. Hence it is more reliable and instant.

3.   Easy Deployment

In-data-lake BI platforms work across whatever combination of platforms enterprises choose to have, providing insights to end users while also simplifying the IT work. The deployment process is also much easier than it is with traditional tools. This flexibility of deployment option is one of the other reasons behind its popularity.

Benefits of a Data Lake in BI Reporting

• Provides a low-cost BI environment
• Eliminates the inherent risk of large builds that do not cater to the actual business requirements. After all, business is always in a dynamic state. What seemed relevant six months ago may no longer be relevant by the time a data warehouse is ready.
• Offers scope for validation of requirement and allows updating of the plan as per evolving requirements. You can check and recheck requirements as frequently and as long as you like.
• Enables quick and frequent analysis as per evolving business conditions. Multiply the accuracy of your data analysis and keep it relevant.
• Design-analysis-build cycles take just 1-2 weeks instead of the 6-12 months required by traditional data warehousing techniques. Enterprises can be decisive with timely insights.
• Less expensive to deploy and maintain since there is a minimal requirement for ETL programs, data modeling, and integration. Cut costs dramatically while increasing your efficacy.

We believe that in future more data will flow from data lake to data warehouse and other analytics tools for all the Business Intelligence reporting. Things that should be taken care of to avert failure of data lake are proper planning, performance, and stability, proper configuration for near real-time reporting, and SQL connectivity with BI Solutions.