When looking for enterprise-level solutions for data management, Data Lake Services are the best solutions that reduce complexity and cost. And with enterprises investing in Advanced Analytics, IoT, and Artificial Intelligence capabilities data lakes have become a de-facto part of the entire data management lifecycle.

But we are still confused about the reality of what they are, how they function, and the advantages they might bring to an organization. Using the word “might” as Data Lake, like any other service has its own challenges, which can be avoided as you will know if you keep reading. 

Introduction to Data Lake

Data Lakes, as the name suggests is used for the storage of data, mainly big data. Big data, as most of us know, deals with the 3V’s Velocity, Variety, and Volume of the data. With the increase in all three of these, the ability of the traditional data warehouses which store structured data starts showing its limitations. 

The key advantage of the use of a data lake is that it stores data in its native format. For example, the data we generate from spreadsheets, Customers profiles data, CRM data, Sales data, Product specifications & key security data, process steps, Internet-of-Things devices, Social Media platforms, Point of sales systems, and Internal Collaboration systems, etc., can be stored. These are just a few of the data sources which have TOMR. But you get the point. 

Data Sources are increasing and they are not necessarily structured and semi-structured. And there are chances you don’t know what to do with the data being generated.

When should you invest in Data Lake Services?

Data Lakes as storage platforms are designed to hold, process, and analyze structured, unstructured, and semi-structured data. And, Enterprises usually invest in Data Laked when they have operational complexity, high operational costs, and require multi-protocol analytics.

You might be thinking then why not just use a Data warehouse, as Data lakes are used in tandem with Data Warehouses, but they are less costly to operate. So, when companies want useful insights from their data, the cost savings can help them invest in other hardware and software, as the data doesn’t need to indexed and prepped. 

Benefits offered by a Data Lake

Data Lake gives you the flexibility of having a collaborative space for multi-structured data. Some of the benefits offered by data lake are:

  • Data Ingestion: Capable of capturing data in native format, irrespective of the type of the data. Does not require prior data schema.
  • Data Analytics: Mostly used by data scientists and data analysts, as it allows exploration of old and new data, and helps them identify the key variables to improve performance.
  • Language support: It not only supports the use of SQL, which most data warehouses require but also has Hive/Impala/Hawq which supports SQL but also has features for more advanced needs. 
  • Collaboration: With the help of data lakes it is easier to distribute data across the entire organization, making collaboration or “data democratization” possible with ease.
  • Schema Flexibility: Traditionally data warehouses have schema predefined, which often acts as a hindrance to data analytics. Data Lakes normally have the flexibility 

By now you must have understood what a data lake is and when can it be used by an organization. Let’s delve a little deep into the stages and implementation part of Data Lakes.

Stages of data-lake development

In general, there are four stages of development for building and integrating data lakes. 

Phase 1: Loading the raw data into data lakes

Phase 2: Use of Data for Analytics  

Phase 3: Integration of Data Lake with Data Warehouse 

Phase 4: Data governance across the organization

Companies might be going through all or any of these phases for building data lakes. But most of the firms should be going through all the phrases. In Phase -1, you would start establishing data lakes and setting up the data to be stored. And in Phase -2, which is one of the main reasons why you want a data lake. You can use it as a test-and-run environment to build prototypes for analytical platforms. With Phase -3 and Phase -4, you can connect the data to make it more organized and easier to access with a schema applied after a basic analysis and make it available for everyone in the organization. This makes the IT organizations have operational applications built on data lakes.

And the key things to note before the implementation would be to think of the future and scale for tomorrow’s needs, focus on what the business needs and outcomes are, check the capabilities and expand the data team if required, and lastly start the process of creating a data governance strategy.

How are data lakes implemented?

Data Lakes are implemented using various data management tools, techniques, and services, along with commercially available tools. For example, Azure, Amazon S3, Snowflake, etc. are available as data lake implementation enablers. The strategy for using data lakes can vary across and will depend on the requirements and ability of the organization. If you think you think you need further help in choosing a data lake, utilize our data lake services.


As you might have understood by now, data lakes are essential for various business reasons and they provide quite an advantage for the organization. The key benefits can be lowered cost of data storage, flexibility with data variety and schema, increased storage capability, and reduce risks for data management of the firm. 

Above all these data lakes form the foundation of a data-driven organization and are needed to link IoT and AI activities in the future. In addition to these, it is important to have good implementation and data architecture for the use of data lakes. If not deployed and designed properly, the success factor of the implementation would be low and in such a case, data lakes can be transformed into data swamps which can be an undesirable situation for our organizations.

With conditions like this, it would be befitting to find the best data lake solutions which are suited and personalized to your organization. So, if you think you are ready for an efficient data management solution and structure, let Polestar Solutions help you by establishing a data solution that works for your business KPIs.