Big data enables organizations to create fresh development prospects while making them constantly competitive with their rivals in the market and offering a seamless consumer experience. Enterprises should have well-curated, high-quality data lakes, though, since they will enable digital transformation across the company and help them succeed in the digital world. A data lake stores large amounts of organized, semi-structured, and unstructured data in its original form. In recent years, data lake architecture evolved to better meet the needs of businesses becoming more and more data-driven as a result of the increase in data volumes. 

What is Data Lake Architecture?

Unlike the hierarchal data warehouse where data is kept in folders and files, a data lake has a flat architecture.  Each data element in a data lake is assigned a special identification and is labelled with a specific set of metadata. A data lake architecture provides high data quantity to improve native integration and analytical efficiency. The Data Lake democratizes data and is a cost-effective approach to storing all of an organization’s data for subsequent processing. Know more about How Business Data Analytics Benefits Organizations?

Building a Robust Data Lake Architecture

In order to build stable data lake architecture, the data lake must have the following features:

A single, shared data repository: Hadoop data lakes preserve data in its unaltered state and record changes to data as well as contextual semantics across the course of the data life cycle. This strategy is particularly useful for compliance and auditing tasks.

Includes task scheduling abilities: The execution of workloads is a need for corporate Hadoop. Assuring that analytic processes have access to the data and processing resources they require, YARN offers resource management and a single platform to carry out consistent operations, security, and data governance services in Hadoop clusters.

Key Elements of Data Lake Architecture

Security: 

It is essential to consider this element, especially throughout the planning and architectural stages. It differs from relational databases, which include an arsenal of security measures.

Governance: 

Operational oversight and monitoring will be crucial for assessing performance and enhancing the data lake.

Stewardship: 

Depending on the company, the role may be assigned either to the owners or a distinct team (users).

Monitoring and ELT processes: 

As data moves from the Raw Layer to the Cleansed Layer to the Sandbox and Application Layer, you need a tool to organize the flow since you will frequently need to apply transformations.

Best Practices for Data Lake Architecture

For a company to successfully capitalize on expanding data volumes and produce fresh insights that spur development while retaining a single version of the truth, digital transformation necessitates understanding the real and correct data sources within the business.

A strong and efficient data lake should meet these requirements:

  • The ability to work with all kinds of data, with high velocity and massive volume.
  • Reduced work to ingest data.
  • Create sophisticated analytics scenarios.
  • Store large volumes of data at a reasonable price.

You might also like to read Why Data Integration in the Insurance Industry is Essential?

To get the insights that support your business’s objectives, you should be able to transform your data and process it through various data operations and transformations. However, these operations rely heavily on the available architecture. In data warehouses, ELT processes are useful for transforming data using a query language and the processing power of the database. The complexity and cost of the project can be significantly affected by the growing number of operations that rely on the database. Hence, many organizations embrace data lakes to reduce friction and complexity in their IT architecture and operations, since ETL tools offer potent engines for in-memory operations and enable various data transformations without creating a database structure.

Why Choose IntoneSwift?

The data warehouse is an essential and quite possibly one of the most integral parts of any team across an organization. This can include departments from IT, data engineering, business analytics, and data science. However, it is true that each of them has its own specific needs for using the warehouse. Intone strives to take a people-first approach to assist businesses with their data analysis processes. We commit to providing them with the best service possible through IntoneSwift which is tailored to their needs and preferences. We offer:

  • Knowledge graph for all data integrations done
  • 600+ Data, and Application and device connectors
  • A graphical no-code low-code platform.
  • Distributed In-memory operations that give 10X speed in data operations.
  • Attribute level lineage capturing at every data integration map
  • Data encryption at every stage
  • Centralized password and connection management
  • Real-time, streaming & batch processing of data
  • Supports unlimited heterogeneous data source combinations
  • Eye-catching monitoring module that gives real-time updates

Contact us to know more about how we can help you!