Data Lake: Why does it matter?

Gleaning business insights through data analysis can help companies outperform competitors in a fast-changing business landscape. Vast amounts of data available to many companies make data analysis even more valuable. But it can also introduce new challenges, both because of the sheer volume of data and because it includes unstructured data from sources such as websites, social media posts, and Internet of Things (IoT) devices. A data lake is a data repository that lets organizations store all this unstructured information with structured information from core business applications and databases so they can analyze it. By exploring this treasure trove of information from multiple sources companies can generate valuable new insights that improve business performance.

What is a Data Lake?

A data lake is a centralized repository that houses data in its native, unprocessed, and raw form. It is designed to accommodate large amounts of data, including structured, semi-structured, and unstructured data from various sources. It can store as little or as much data as the organization requires. It is equipped to process and organize this raw data irrespective of its size and volume, offering high analytics performance and native integration.

It stores this large amount of raw data in a flat architecture with metadata tags and a unique identifier for easy and quick retrieval. Essentially, a data lake enables enterprises to gather any type of data from any source without having to first structure it and enables them to analyze it using analytics applications or languages like Python, SQL, or R.

Elements of Data Lake

• Data ingestion: It is supported by “connectors” and other services that import data from multiple structured and unstructured sources.
• Secure storage: The data lake must be able to store and protect a vast and expanding volume of data. The infrastructure supporting the data lake should scale easily and at an appropriate price because it’s seldom possible to predict all future sources and volumes of data. It also needs to be protected against system failures and unauthorized access.
• Governance and curation: Businesses need to decide which data is imported into the data lake and how to manage it. The data also needs to be cataloged so users can find it. Without governance, data lakes can deteriorate into data swamps: pools of disorganized, stagnant data that languish unused and provide little value to the organization.
• Processing and analytics: The data lake should support a wide range of analytic tools because people will use the data lake for different types of analysis.

Why should an organization use it?

A data lake is best suited for storing data that doesn’t need to be used immediately. Since there is no predefined schema, the data retains all its original attributes, allowing for harmonization later. It is increasingly becoming a favorite with businesses across industries because they provide an unrefined view to data analysts and are also cost-effective since the data is processed only when the need arises. Some of the other reasons why businesses are choosing it over data warehouse include:

• Centralized Data: It provides single storage for massive amounts of data. The centralized repository prevents data silos.
• Higher Quality of Analysis: The diverse and raw format of the data present in a data lake provides analysts with a robust and higher quality of analysis by presenting data in its original form. It is convenient to employ AI/ML techniques to data to gain important business insights.
• Schema on Read: Data lakes store any type of data, so there is no need to process it into any schema. The data is kept raw until it is needed for analysis, which is called “schema on read.” Schema is only applied when data needs to be analyzed. This saves on processing times during the ingestion of data into the data lakes.
• Flexibility: Users can access and explore data in data lakes without moving it into another system. Given that insights and reports from a data lake can be pulled on an ad-hoc basis, it offers more flexibility in data analysis.
• Competitive Edge: Organizations gain a competitive advantage since better forecasts can be made with the raw data in data lakes. The analytical experiments also enhance the efficiency of business decisions.
• Data Democracy: Users across the organization, from different departments, levels, and teams, can access and perform a range of analytics on the same set of data.

Major benefits of using Data Lake

• Helps fully with product ionizing & advanced analytics
• Offers cost-effective scalability and flexibility
• Offers value from unlimited data types
• Reduces long-term cost of ownership
• Allows economic storage of files
• Quickly adaptable to changes
• The main advantage is the centralization of different content sources
• Users, from various departments, may be scattered around the globe can have flexible access to the data

Some risks

• After some time, it may lose relevance and momentum
• There is a larger amount of risk involved while designing a data lake
• Unstructured data may lead to ungoverned chaos, unusable data, disparate & complex tools, enterprise-wide collaboration, unified, consistent, and common
• It also increases storage & computes costs
• There is no way to get insights from others who have worked with the data because there is no account of the lineage of findings by previous analysts
• The biggest risk is security and access control. Sometimes data can be placed into a lake without any oversight, as some of the data may have privacy and regulatory need

The challenges of Data Lake

The main challenge is that raw data is stored with no oversight of the contents. For a data lake to make data usable, it needs to have defined mechanisms to catalog, and secure data. Without these elements, data cannot be found, or trusted resulting in a “data swamp.” Meeting the needs of wider audiences requires data lakes to have governance, semantic consistency, and access controls.

Conclusion

Data lakes can help organizations respond more quickly to the ever-changing business landscape. They let businesses quickly aggregate unstructured and structured data from many different sources into a single store for analysis. Many different users can employ a variety of analytic tools to explore answers to new business questions as they arise. A well-implemented data lake can deliver business insights that drive improvements in business performance.