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Creating Data Lake? Step by step approach

Creating Data Lake

In case if you are looking to create data lake, below we have covered step by step approach which you have to follow.

(A) Data Source Discovery and Assessment:

   – Identify and document all the potential data sources that need to be ingested into the data lake/warehouse.

   – Assess the structure, format and volume of data from each source.

   – Understand the data quality issues, such as missing values, duplicates, and inconsistencies.

(B) Data Lake Architecture Design:

   – Define the data lake architecture

   – Decide on the appropriate file formats

(C) Data Ingestion:

   – Develop data ingestion pipelines to extract data from various sources and store it in the data lake’s raw zone.

(D) Data Transformation:

   – Perform data transformations, cleansing and standardization as needed for various reporting requirements.

   – Implement data quality checks and validation rules to ensure data integrity.

(E) Data Organization and Cataloging:

   – Establish a consistent naming conventions for the data lake.

   – Use data catalogs or metadata management tools to document and maintain metadata, facilitating data discovery and governance.

(F) Data Access and Analytics:

   – Provide access to the curated data in the data lake through various tools and interfaces  like Helical Insight (BI).

   – Implement access control and security measures to ensure data privacy and compliance.

(G) Data Governance and Lineage:

   – Establish data governance policies and processes to manage data quality, security and compliance.

   – Implement data lineage tracking to understand the flow of data from sources to consumption points.

(H) Monitoring and Maintenance:

   – Monitor the data ingestion, curation, and processing pipelines for performance, errors, and potential issues.

   – Implement automated processes for data retention, archiving, and lifecycle management.

(I) Documentation and Training:

   – Document the data lake architecture, data sources, transformation processes etc.

   – Provide training and support to data consumers, IT team and stakeholders for effective usage.

The key differences from a data warehouse approach are the focus on scalability, flexibility, and handling diverse data types (structured, semi-structured, and unstructured) in a data lake. The data lake architecture allows for a more exploratory and schema-on-read approach, enabling faster ingestion and deferring data transformations until they are needed for specific use cases.

Tools like Ask On Data, with its simple chat interface powered by AI, can help you simply type and load the data into the data lake as well as do the required transformations. It can help in saving around 93% of time in creating data pipelines as compared to tradition ETL tools. Aside, since it will do the processing outside the data lake, it can also help you in saving costs as well.

If you are looking for some professional guidance you can reach out on

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