Data allows organizations to effectively determine the cause of problems and can be used to measure and record a wide range of internal and external business activities. While the data by itself may not be very informative, it is the basis for all reporting and is crucial in running any business. This indicates the extremely high value that is attached to data in our information-driven world. Data Lake is one of the more recent innovations in the field of Data Storage and Management.

Data Lake is a centralized repository that allows users to store structured data (rows and columns from RDBMS), semi-structured data (CSU, XML, JSON), unstructured data (emails, documents, PDFs), and binary data (images, audios, videos).

Compared to other methods of data storage, Data Lakes are cost-efficient and are an economical option for many companies. According to reports, the Data Lakes Market was valued at 3.74 billion USD in 2020 and is expected to reach 17.60 billion USD by 2026.

What is a Data Lake and How does it work?

The term “Data Lake” was coined by James Dixon, CTO of the business intelligence software platform Pentaho, to imply that the mechanism of Data Lake storage can be compared to a still-body of water in its natural state.

Being a storage repository, Data Lake can hold immense amounts of raw data such as object blobs or files in its native format. Data Lake can store both single stores of data such as source system data, sensor data, social data as well as store transformed data such as data used for reporting, visualizing, advanced analytics, and machine learning.

Data Lakes allow you to import and store any real-time data collected from multiple sources in its original format. This data can be extracted quickly using a variety of data storage and processing tools. This process allows you to scale up to data of any size while saving time in defining data structures, schema, and data transformations.

Advantages and Key Attributes of Data Lake

Data Lakes provide an optimum solution that helps to deal with the higher expectations from users and deal with greater data volumes and varieties. It is becoming increasingly relevant in designing enterprise data strategies.

Here are a few benefits of using Data Lakes:

  • Faster User Access: Data Lake loads, stores, and preserves data in its original form. This makes it easier for data owners and users to access and consolidate data eliminating the usual technical roadblocks present in other forms of data storage. It offers faster data retrieval when compared to traditional Enterprise Data Warehouses.
  • Easy Data Management: In general, Enterprise Data Warehouses process data in many forms (transformations, aggregations, and data updates) thereby becoming a challenge to consolidate data when required. Data Lake tackles this problem by capturing any changes made to the stored data continuously throughout the Data Lifecycle.
  • Scalability and Flexibility: Data Lakes offer relatively low-cost scalability. It is also flexible and allows enterprises to upload anything from raw data to fully aggregated analytical The structure of Data Lake is schema-free thereby allowing users to define multiple schemas for the same data enabling them to decouple schemas from data.
  • Improved Data Analytics: Data Lake uses deep learning algorithms to utilize the availability of large quantities of coherent data which immensely helps in real-time decision analytics.

Data Lake Usage in the Real World

As they host raw unprocessed enterprise data, the size of Data Lakes can be in the range of a few hundred Terabytes to sometimes even Petabytes.

The need for global accessibility has ensured that Data Lakes are also implemented in cloud-based distributed storage systems like Snowflake. Snowflake is a flexible solution strategy based on Data Lake, with a cloud-built architecture that can meet a wide range of unique business requirements. Snowflake allows users to execute a near-unlimited number of concurrent queries without impacting performance. This platform offered by Snowflake provides both the benefits of Data Lakes and cloud storage.

The advantages offered by Data Lake, in conclusion, enable its usage over a wide range of business applications, mobile applications, IoT devices, and even social media. Data Lakes along with the other technologies up and coming in the field of Data Analytics and Management is here to stay for the foreseeable future.