Data Lake – The Centralized Data Storage and Management Repository

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We are living in the age of data and according to Gartner; the volume of worldwide information is growing every day at a minimum rate of 59 percent annually. While managing such a large volume of data may seem to be a significant challenge, variety and velocity makes it even more difficult.

It is also very evident that generation of larger and larger volumes of data will continue to pile, considering the exponential growth of the number of handheld and Internet-connected devices.

This is true for some organizations that have systems of engagement but not for others where the data volume growth is not very high. Data volume is different for different organizations. Nevertheless, meaningful and useful analytics is an important factor for every stakegathering into one central repositoryholder.

With the increased use of tools available for different functionalities across organizations, the task of generating meaningful and useful reports is becoming more and more challenging. Data Lake can help to overcome this problem.

What is Data Lake?

The term ‘data lake’ was first used in the year 2010 and its definition is still evolving. In general, Data Lake refers to a central repository capable of storing zettabytes of data drawn from various internal and external sources in a format that is close to that of raw data.

The idea is simple. Instead of storing data in a built-in storage, you can directly move it into a data lake.

Speaking in broad term, Nick Heudecker, Research Director at Gartner, explains data lakes as an “enterprise-wide data management platforms for analyzing disparate sources of data in its native format”.

This eliminates upfront costs, like transformation of data for ingestion. Once data is placed into the lake, it is available for analysis by everyone within the organization. Thus, a data lake helps organizations to gain insight into their data by breaking the data silos.

Challenges of Data Lake

Challenges of Data Lake

A data lake is usually thought of as the collection and collation of all enterprise data; from legacy systems and sources, data warehouses and analytics systems, third-party data, social media data, clickstream data, and anything that might be considered useful information by the enterprise.

Although the definition is interesting, is it actually possible or required by every organization?

Different organizations have different challenges and patterns of distributed data and with this diversified scenario, every organization has their own need for Data Lake. Though the needs, pattern, source of data and the architecture are different, the challenges are same with respect to building a central storage or lake of data. Some of the challenges have been discussed briefly below –

  • Bringing data from different sources to a common central pool
  • Handling low volume but highly diversified data
  • Storage of data in a low-cost infrastructure compared to Data Warehouse or Big Data
  • Real-time synchronization of data within a centralized data storage
  • Traceability and governance of centralized data

Considerations before implementing Data Lake

In most cases, data lakes are deployed as a data-as-a-service model. It is considered as a centralized system-of record that serves other systems on an enterprise scale. A localized data lake not only expands support to multiple teams but also spawns multiple data lake instances to support larger needs. Different teams can then put to use the centralized data for their analytical needs.

With all these understanding, it is now time to discuss about the various needs of Data Lake in terms of Integration and Governance.

1.  Integration Challenges

In order to deploy a Data Lake at enterprise level, you need to have certain capabilities that will allow it to be integrated within the overall data management strategy and IT applications as well as within the data flow landscape of the organization.

  • It is therefore very important to make sure that the lake is getting the right data at the right time. For example, a data lake may ingest monthly sales data from enterprise financial software. If the time of data intake is too early, then only a partial amount of data set or no data will be saved. This could lead to inaccurate reporting down the line. Thus the integration platform operating in the background should be capable of pushing data from various tools in both real-time and on-demand based on business case.
  • Though the main purpose of Data Lake is to store data, there are times when some data will need to be distilled or processed before being inserted into the data lake. This depends on the different business cases. Thus the integration platform should not only have support for this but also ensure that data processing is happening accurately and in correct order.
  • A centralized data storage is useful only when the stored data needs to be extracted for use by all different departments. There should be a capability of integrating Data Lake with other applications or downstream reporting/analytic systems. The Data Lake should also have support for REST APIs using which different applications can interact or get to push their own piece of data.

2.  Governance of the Lake

Data Lake is not only about centrally storing data and furnishing it accordingly to different departments. With more and more users beginning to use Data Lake directly or through downstream applications and analytical tools, the importance of governance for Data Lake increases.

  • Data Lakes create a new level of challenges and opportunities by bringing in diversified data sets from various repositories, which are then gathering into one central data repository for all business data.
  • The major challenge is to ensure that data governance policies and procedures exist and are enforced into the Data Lake. There should be a clear definition of the owner for each data set as and when they enter the lake. There should be a very well documented policy or guideline regarding the required accessibility, completeness, consistency and updating of each data.
  • Hence, there should be some built-in mechanism in the Data Lake for tracking and recording manipulation of data assets present in the Data Lake.

Is Data Lake same for all?

Implementation of Data Lake is not same for all organizations as the volume of data and the requirement of data collection varies from organization to organization.

Speaking for a general point of view, Data Lake comes with a perception that the data volume should be at a level of petabytes or zetabytes to be precise. Hence, it needs to be implemented using a NoSQL DB.

However, in reality, this amount of data volume and implementation of NoSQL DB may not be needed or possible for all organizations. Keeping the end goal of having a central data store that caters to all analytical needs of an organization, Data Lakes can be started using a SQL DB and with considerable data volume.

Data Lake – Kovair solution

Kovair Omnibus has been one of the market leaders in the domain of data integration for the last few years now. After having marquee customers from semiconductor, networking, manufacturing, telecom, and banking & finance over the last several years, Kovair has witnessed the shift of focus for organizations using data from multiple tools and different teams.

Though Kovair Omnibus provides the support for features like cross tool reporting, traceability, and task based workflow, it was not fulfilling their total needs for analytics. Customers came to us with the need of a central data store where data needs to come from all different tools getting used in the organization irrespective of whether they are part of the integrated ecosystem or not.

Keeping this need of the customers in mind and the market shift in terms of analytics, Kovair has leveraged its huge expertise in the field of integration to come up with a solution in the field of Data Lake.

What we offer

Kovair Data Lake is a centralized data store having SQL Server as the database. It is capable of storing data from multiple projects residing in diversified tools that are getting used in the organization. If the tools are integrated through Kovair Omnibus then data gets pulled from Omnibus data store through Omnibus extractors and if not then the data gets pulled directly from the tools through data extractors. Based on the need of the organization, data can be segregated by departments or business unit or any other criteria by having multiple instances of Data Lake. Kovair Data Lake also comes with a very intuitive UI interface for managing and monitoring of the Data Lake.

Kovair Data Lake

Kovair Data Lake licensing model

The licensing model of Kovair Data Lake is very simple and customer friendly. It is a subscription based model as given below.

  • Large Data Repository: Annual Subscription for each Data Lake Instance
  • Tool Extractors: Annual Subscription for each tool Extractor
  • Web Portal: Annual Subscription included with the Data Lake Instance
  • Omnibus Extractors: Annual Subscription for each Kovair Platform Extractor


Over the last several years, Kovair has successfully catered to the integration needs of many Large, Medium and Small organizations spread across various industry verticals. Kovair Data Lake is one more feather in the same cap. With Kovair Data Lake being launched, Kovair will now not only cater to the integration needs of an organization but will also cater to their need for central data satisfying their analytical needs. Kovair Data Lake will also have integration capabilities with some of the popular BI tools.

Amit brings with him expertise in DevOps, Big Data, Application Lifecycle Management (ALM), Agile Implementation using both Scrum and Lean, SDLC and PPM domains spanning over 18 years. Currently, he strategizes on the growth, market direction, and customer engagements for Kovair's exclusive range of ALM and Integration products. Until his recent assignment, he has held the responsibility of defining new products and marketing plans for them. Amit holds a Project Management certification from PMI Institute and is also a Certified Scrum Master.

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