How Data Lake Can Be a Game Changer!

For all those, who are using any sort of data on a day-to-day basis have you ever wondered, how the data is being generated at the back end? And for the IT or the Data Management people do you find it hard to collect, load, and transform the data according to the requirements of people? Think about it.

In recent years where has the data been generating: Customer data, Point of Sales data, Product data, Process data, Social Media data, and put the word data with anything and voila you might have it. And for the processing of these data, you might normally think of having a Data Warehouse in place. But do you really know what you might be using the data for?

Understanding the need for Data Lakes

Data Warehouses, as most of us will be aware of, are a structured method of storing the data with the schema, purpose, and the end-user already defined, and the data is tailored to suit their needs and requirements. But with the increase in the types of data being generated, we cannot have an idea about what the end-user might want. Even lasers were dubbed as “A solution looking for a problem”. Similarly, you might never know what use your data might have hidden it.

With the growing Data Scientists, Analysts, and Solution architects the enormous potential of data can be found out. But obviously, there is one problem- Roughly 1.145 trillion MB per day data is generated (Ok! This is the global data generated, but even firms generate large amounts of data) i.e. where to store it?

Data warehouses, though they are far cheaper than what they used to be in the last decade still cost money depending on the storage capabilities, and with the addition of required structure beforehand Data warehouses are not the ideal solution for this problem. Here is where Data Lake services come into prevalence.

Data warehouses and Data Lakes co-exist and that would be optimum. But before we speak about it, we need to understand the importance, Business advantage, and key benefits of a data lake.

What is a Data Lake?

Bored of the standard definitions? We are too! Imagine it being a sea, where all the water from all the rivers joins, in this case, data from all the sources. Unlike an actual sea, the individual data particles are not lost but can be accessed with their meta tags, etc. So, though the name says Data Lake (in my opinion it should have been Data Sea), it acts as a repository of all your data in their original format. Sounds simple right. Let’s understand a little about their structure and the different layers present in them.

Layers of a Data Lake

  • Data Sources: Data sources are where original data, a variety of internal and external sources, reside in their original format. It can either be operational, transactional, or analytical data sources like ERP, CRM, PoS, etc. or website analytics, content popularity, etc., or non-structured data like images, videos, audios, etc.
  • Raw/Landing Layer: Data is extracted from multiple source systems and stored in a raw, original format in the landing layer of the data lake. The Raw layer tags the data for the source system, which will be useful for the extraction of data in the future.
  • Standardized Layer: As data comes in different formats (Relational, JSON, Binary, CSV, etc.) data needs to be standardized into the rows and/or columns format: more commonly known as the Relational Database format. This layer applies business logic and also transforms the data.
  • Curated Layer: This layer is created as per business requirements and it can have data marts (smaller versions of data warehouses) for reporting and analytics. It can have de-normalized data for data scientists, who can further access and modify the data as per their requirements.

Five Business Benefits Of Data Lake As A Service

With many organizations having Data pipelines and integration large volumes of data, it is also important to understand the benefits data lakes provide for a business:

  1. It integrates with and expands the current enterprise data warehouse (EDW)
  2. Reduces expenses to buy costly licenses and can reduce the burden on existing data warehouses.
  3. Removes the barriers by separating all enterprise data and creates the ability to bring together all the data.
  4. Unified view of data and access to Self-service analytics and visualization platform.
  5. It provides a prebuilt cloud service that abstracts the complexity of the underlying platform and infrastructure layers, so organizations can use these services without having to install or maintain the technology themselves.

How does data lakes extraction differ from Data warehouse?

As it is commonly known as ETL or Extraction Transformation and Loading is the process used in Data Warehouses. But in Data Lakes the process is ELT or Extraction Loading and Transformation. In Data Lakes, the process itself starts by extracting the raw data from various sources using APIs or connectors and it is loaded by tagging the data sources. The Transformation phase includes data cleansing, data standardization, business logic, etc.

Some words of caution

Though we are talking about how data lake services will be beneficial for your business, it is important to know how to utilize them. If companies lack governance, lack the tools and skills to handle large volumes of disparate data there is a high probability the data lake will transform into a Data Swamp. Storing data of all types and varieties in a central platform sounds good on the surface but can create additional issues, which can definitely be handled and taken care of with the right data governance plan.

In conclusion, we don’t know what the future holds. But having flexibility and the leverage to utilize your data will be important in the future. This can be only done by the presence of Data Lakes, and a better version of this is by using Data warehouse and Data Lake in tandem as we were talking about earlier. But if you think you want to reduce the cost of data storage, increase storage capacity, and store a wide variety of data types, and lower the risks for data management across the enterprise. It might be the right choice for you. If you still need further assistance with data lake for business data needs, you can always contact us and we will be more than happy to help you!



Polestar Solutions | Data analytics company

As an AI & Data Analytics powerhouse, PolestarSolutions helps its customers bring out the most sophisticated insights from their data in a value oriented manner