How to build an efficient Data Lake for modernizing your business?

Data is the lifeline of the modern business world. The exponential growth rate, at which data is generated, recorded and churned every year, has left our planet saturated in information scattered all over. This downpour of data can be tamed by a Modern Data Platform approach and advanced technologies such as machine learning, business intelligence, and artificial intelligence, to turn data analysis into an innovative approach for strategic decision making. Eventually, the need for easy accessibility, scalable, and flexible data anywhere, anytime, can be addressed by building a unified data repository or Data Lake across the enterprise and making it available for analysis by everyone in the organization. The Data Lake based on the new edge technologies establishes a Modern Data Platform that resolves the issue of scalability for handling this exponential data growth and otherwise unacceptably high costs.

But, building a Data Lake that is efficient and meaningful for the businesses is one of the most common challenges that organizations face. There have been numerous instances of failure to set up an efficient Data Lake. According to Gartner, through 2018 80% of Data Lakes will not include effective metadata management capabilities, which will make them inefficient.
Datametica Solutions mitigates the pain areas and increase the value proposition for the data, by finding new ways to utilize it. With extensive production experience in working with Cloud and Big Data technologies, to develop well-governed Data Lakes, high-performance framework solutions and best practices have evolved, both for cloud and on-premise architecture. This has established Datametica with a distinguished track record across a network of Fortune 500 clients for solving complex problems.

Here comes the quick rundown of some critical parameters followed by Datametica to build an effective Data Lake on a Modern Data Platform:

Data access pattern:

With a prime focus on the future state data model for the way data is stored is a key foundational practice. However, in order to excel in this fast-paced data-driven business world, it is important for an enterprise to set up a Modern Data Platform that is user-friendly and inherently analytics-enabled. To provide a user-friendly platform, understanding the data access pattern is important, which can imply the future-state Data Model. When done manually, this is a very challenging and cumbersome process and is susceptible to major failures when upgrading the data platform. With their rich technical expertise and investments, Datametica has developed intelligent technology, called Eagle, that provides automated discovery of data access patterns across data stores, which recommends the future state data model and platform. Eagle further helps in tuning and optimizing the platform, data and access patterns

Selection of the platform:

The modernization of legacy data system to a Modern Data Platform Data Lake must address the legacy system limitations, expense, rigidity, scalability and responsiveness. Datametica recommends a future state platform, which leverages services as serverless architecture, managed services, data locality, decoupling of storage and compute, cost transparency, pay-per-use model and rich library for analytics.

Strong Foundation set up:

It is important to set up a solid foundation for the Data Lake, with defined protocols, processes, and tools as a baseline. Once the data platform and optimized data model is prepared, there are several aspects that need to be decided such as determining the tools and technologies to be used, ensuring the security of the platform, deciding the computational processes, setting up the serving layer and much more. In a nutshell, the foundation of the data lake needs to be set up upfront even before any use case is started.

Data on-boarding:

To ascertain optimized data absorption in the Data Lake, an ingestion framework can be implemented to integrate with various source systems such as Databases, CDC and Logs data. The Data Integration is done in the ingestion layer, which will be built to handle various data patterns like batch, micro-batch, and real-time ingestion. Datametica has devised a framework to onboard data from the various data source such as databases, file systems and data streams to Data Lake. It brings the higher degree of automation to onboard data with zero manual work.

Workload modernization:

Once the data is onboard, the next step an enterprise should focus on modernizing the workload running on the new data lake. While onboarding the workload, a requisite of code conversion may arise when there is a discrepancy in the code of your existing platform and that of the future state. This code conversion is efficiently done by using an automated code converter created by Datametica called Raven.

Validation:

Profile the data to detect anomalies. Understanding the taxonomy of data becomes important to achieve optimized data sets. With continuous data validation and direct workload comparison between the existing environment and the Data Lake, it is ensured that the curated data is migrated to the Data Lake. Datametica brings in an automated tool called Pelican, which can perform effective data validation without any physical data movement.

Security:

Keep track of data and access everywhere! When data is the foundation of your existence in the business world, you know the importance of keeping it safe. Data from multiple sources comes with new vulnerabilities. While offloading legacy data into the Data Lake, emphasis should be given to keep it secured for protecting the private and sensitive information. Datametica provides protection to business-critical data to the maximum limit, by data encryption (both in rest or in transit), data masking, data ingestion security, access control, authentication and authorization, access node security, firewall installation and many more.

Data governance:

Datametica can help in implementing a sturdy Data Governance through our product called eCat which establishes efficient metadata management, the quality of the data and end-to-end data audit and lineage. This mechanism ensures that the data is consistently defined across the enterprise by fulfilling major gaps and missing functionalities. It is a Collaborative, Automated, Intelligent platform that helps to navigate data at various level in an organization.

Pertaining to the above parameters, an efficient Data Lake on a Modern Data Platform can be implemented, which will help optimize the operations of an enterprise. We at Datametica, help you in setting a Modern Data Platform and Data Lake that can effectively help you to save cost, scale your capacities, use your data for advanced analytics across the platform and modernize your business. With our unique toolsets and accelerators, we provide an end-to-end solution from assessment to ongoing maintenance, for migrating optimized data, workload and use cases. Thus, ensuring effective deployment, reconciliation, and operation, the Data Lake we implement is a value addition for the enterprises.

Either follow the traditional way with legacy systems and compromise on your business capabilities or build an efficient Modern Data Platform and Data Lake through us and get ready to be at the front line of the contemporary business world.

Explore More

 

Top