Decipher the Mystery of Data Lake Architecture

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James Dixon, Pentaho CTO coined the term “data lake.” A data lake is defined as the advanced version of the traditional data warehouse concept – source type, processing type along with the structure that operates providing business solutions. 

Data lakes are generally implemented via cloud providers, they’re architected with multiple data storage and data processing tools. As such that they’re able to manage service-based services that have been associated with processing and maintaining data infrastructure for a data lake. 

How do you define a data lake (in simple terms)?

A data lake is a collection of various data assets that are additional to the originating data source. These assets are then stored in the near or exact copy of the source format. The main objective of a data lake is to present the unrefined data to the most highly skilled analysts. These data are then refined and analyzed using techniques that may exist in a traditional data store such as data warehouse or data mart. 

A report by Gartner predicts 80 percent of the successful CDOs to have value creation to be their first and foremost priority by 2021. 

Ever wondered why we need data lake architecture? Even though the concept of data lake has been here for a while, organizations still find it a challenge to understand the concept. Well, for organizations to understand the value of big data they need to first understand the concept of data lakes and to be able to extract maximum value from the data ecosystem. 

Let us further dive deep into the concept of data lake architecture, what it is and why we need it?

A data lake architecture majorly comprises of three components: –

  1. Sources – sources are the providers of business data to the data lake (where datasets are stored). As such that the ETL and ELT mediums have been used for extracting data from other sources for further processing of the data. Sources could be anything from heterogenous to homogenous. However, data lake architecture majorly used sources extracted from business applications such as databases or file-based data storage applications or transaction business applications such as CRM, ERP, and SCM that capture business transactions. Other sources include IoT sensors, device logs, SAAS applications, and multiple documents – .CSV and .Txt in file formats and JSON, XML, and AVRO used for data lake projects
  2. Data processing layer – data processing layer comprises of metadata store, data store, and the replication that supports high availability of data. These indexes are applied to the data to optimize the process. One of the best practices includes the cloud-based cluster for data processing. The data processing layer is specifically designed to support the scalability, resilience, and security of the data. Tools and cloud providers play a major role in supporting the data processing layer such as Azure data bricks, Data lake solutions from AWS, and Apache Spark. 
  3. Target – once the data lake is done processing with the data, it is then projected to the target application or targeted system. You will find multiple systems that consume data from data lake with the help of connectors or API layers. Analytics dashboard, EDW, machine learning projects, and data visualization tools extensively use data lake. 

The significance of data lake in businesses: –

A data lake acts as an enabler for businesses that initiate in making positive business decisions and market analytics solutions. This also helps in an IT-driven business process. Moreover, cloud-based data lake implementation helps businesses create cost-effective decisions. This implementation is crucial for business making decisions before deciding which tools and technologies to be used in a technical decision. Tech professionals such as DevOps engineers, data scientists, data engineers, and data analysts gather to create successful data lake implementation for business purposes.

Also Read: Top Resources to Develop Data Science Skills

Data lakes Vs Data warehouses 

Most people often get confused between the terms “data lakes” and “data warehouses.”

Both are widely used for storing data, however, they’re not interchangeable terms. Let us look at the differences. 

Data lake – is a pool of raw data that is not defined.

  • Data structure – raw
  • Purpose of the data – yet to be determined 
  • Users – used by data scientists 
  • Accessibility – highly accessible and it is quick to update 

Data warehouse – it is a repository for structured and filtered data that has already been processed for a certain purpose. 

  • Data structure – processed
  • Purpose of the data – currently in use 
  • Users – business professionals 
  • Accessibility – it is more complicated and difficult to make changes

The next question might be, which is the right approach? Data lakes or data warehouses?

In present times, most organizations require both. Data lakes are required to harness big data and gain benefits from raw and unstructured data for machine learning. However, you will still need a data warehouse for analytics to be used by the business users. Some of the sectors making extensive use of data lakes – finance, healthcare, transportation, and education.

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