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Data Lake

How to build an efficient Data Lake to update the business?

A modern database platform is formed based on Data Lake. These days we heard many cases for Data Lake as a service, data lake, data lake implementation, and cloud-based hybrid data integration to increase data maturity and use for business insights.

As we know, data lakes allow visibility to your data that breaks down silos around your business by storing all incoming data. The basics data lakes refer to unstructured or semi-structured data that is a central repository in a single place. HDFS is a distributed file system created the first version of Data Lake that is known by the Hadoop file system. 

As per the Aberdeen review, associations who executed a Data Lake beat comparable organizations by 9% in income development. 

Data Lake has a flat architecture, unlike a traditional Data warehouse where data is stock up in the form of the folder and some files. Here data elements in a is given a sole identifier and labeled with a set of metadata.

As shown in the image, data collected from various sources stored to the data lake in the original format and then processed to various fields as required. Organizations face some problems due to the increase in data from various sources. And here comes the role of a data lake platform that helps the business to face challenges by maintaining an infinite data lake.

Why is Data Lake required?

Businesses need their data synchronized as it consists of multiple departments, and every department has different requirements of data and its processing. So enterprise wants to analyze data a separate data lake according to the requirements to make insightful business decisions.

This necessity fits very well in the undertakings which have various divisions or organizations which need access to devices and information. 

Data scientists or data science organizations need their data researchers and investigators to play with the information while settling on basic business choices to fuel business development. The focal point of Data Lake as a Service is to characterize endeavor wide detailing methodology to make readiness and versatility. 

Perhaps the most significant advantage of an information lake is the adaptability to drive your business forward through nimble examination that can gauge execution and improve efficiency by making educated decisions. 

What is the solution to implement a data lake?

A client demands the information in their private space, i.e., their data lake engineering, where they can change and investigate the information as required. Data lake usage gives a self-administration entry where every one of the clients approaches the authoritative information as indicated by their jobs in the association and strategies. 

The clients are charged compared to the time and utilization of nature. The earth provisioned naturally on demand endorsement and just the clients who have mentioned full oversight over the earth. 

If the unstructured data that stores in a data lake are not well-curated, it may overflow with irrelevant information that, in the end, difficult to manage and may lead to a data swamp.

Nature can be de-provisioned, consequently on the fulfillment of their solicitation. Complete information security, encryption, and concealing according to hierarchical approaches with the goal that no information is undermining. 

Data lakes operate on the ELT strategy:

  • Extract data from various sources like user log in, e-commerce websites, mobile apps, social media, and more.
  • Load data in the data lake, in its original format.
  • Transform it to gain significant insight as per the specific business requirement.

Beating all the challenges, the big data company develops real-time data pipelines while keeping data security as the priority. This change brought data to the forefront of the company’s architectural decisions.

Making a Data Lake for your Business

If you own a business and thinking to start creating a data lake this is the right time to make sure that different data sets are added consistently over long periods of time. One should go with selecting data lake technology and relevant tools to set up the data lake solution.

  • Identify data sources
  • Set a data lake solution
  • Process and automation 
  • Ensure the right authority

Data lake is immutable with high authenticity:

  • Ease of access to information: Not just completes an information lake store data originating from different sources; it additionally makes it accessible for anybody needing the required information. Any business framework can inquiry about the information lake for the correct information and characterize how it is prepared and changed to infer explicit bits of knowledge. 
  • Cost viable: Data lakes are a single-stage; financially savvy answer for putting away enormous information originating from different sources inside and outside the association. Coordinating an information lake with your cloud is another alternative that enables you to control your expense as you pay for the space you use. Since information lake is fit for putting away a wide range of information and effectively versatile to suit developing volumes, it is a one-time speculation for endeavors to get it set up.
  • Security: Although anybody can uninhibitedly get to any information in the lake, access to the data about the wellspring of that information can be confined. These make any information misuse, past necessity, troublesome. 
  • Ease of use of information: The original data put away legitimately from the source enables the more remarkable opportunity of utilization to the data searcher. Information researchers and business frameworks working with the information don’t have to stick to a particular configuration while working with the information.
  • Diverse sources: Generally, information vaults can acknowledge information from restricted sources, after it has been cleaned and changed. These are independent of the structure and organization of the information and guarantees that information from any business framework is accessible for utilization, at whatever point required. Dissimilar to those data lakes store information from an enormous scope of information sources like online life, IoT gadgets, versatile applications, and more.
  • Analytics: Data lake engineering, when coordinated with big business search and examination procedures, can assist firms with getting bits of knowledge from the vast, organized, and unstructured information put away. A data lake equipped for using enormous amounts of sound information alongside profound learning calculations to recognize data that forces ongoing progressed examination. Preparing crude information is extremely valuable for AI, prescient examination, and information profiling.

Best practices for data lake implementation:

The primary goal of building an information lake is to offer a grungy perspective on information to information researchers. Brought together tasks level, handling level, refining level, and HDFS are significant layers of information lake design. Data ingestion, data investigation, information stockpiling, data quality, data examining are some significant data process types.

  • The Data Lake engineering ought to guarantee that the abilities vital for that space are an inalienable piece of the plan. 
  • Architectural parts, their communication, and recognized items should bolster local information types. 
  • Faster on-boarding of newfound information sources is fundamental. 
  • Data Lake should bolster existing endeavor information the board procedures and strategies.
  • Data Lake causes modified administration to separate the most extreme worth. 
  • The plan of Data Lake ought to be driven by what is accessible rather than what is required. The outline and information prerequisite isn’t characterized until it is essential. 
  • Data disclosure, ingestion, stockpiling, organization, quality, change, and representation should ready to oversee autonomously. 
  • The configuration should ready to manage by dispensable segments incorporated with administration API. 

Associations that effectively produce business esteem from their information that beats their companions. The pioneers had the option to do new sorts of examinations like Artificial Intelligence over new sources like log records, information from click-streams, web-based life, and associated web gadgets put away in the information lake. 


These helped to recognize, and follow up on open doors for business development quicker by drawing in and holding clients, boosting efficiency, proactively looking after gadgets, and settling on educated choices. We at Sigma Data Systems, a data lake implementation company sorts out workshops with clients to talk about their prerequisites in detail, share our encounters, conceptualize over the difficulties, business use cases and convey this inside half a month of exertion. 

Meghavi Vyas

Meghavi Vyas

Meghavi is Sr. Technical Writer and exploring the knowledge in Big Data and Analytics. She is passionate about new technology and got her hands on writing technical terms and data aspects. She loves to explore the bleeding edge of tech stuff as an early adopter to Data Science.