What Exactly is a Data Lake?

A data lake is a centralized repository that allows you to store all your data at one place, from all different sources or with different schema like structured, semi-structured and unstructured data at any scale.
This means data is stored as-is in raw format and structured only when needed for analysis(ELT), that's provide the two concepts in the data engineering ELT vs ETL and providing tremendous flexibility using the ELT with the cloud-native object storage (e.g. AWS S3, Google Cloud Storage, Azure Blob) which make ingestion of raw data as it is and at any scale. At the same time while loading the data into the data lake (using the storage in cloud AWS S3, GCP GCS, Azure Blob) Avocado Datalake engineer's expertise helps you to utilize the open table format like Delta Lake, Apache Hudi or Apache Iceberg to provide the data discovery through the data cataglog and data governance and security using the cloud-native services like AWS Glue, GCP Data Catalog, Azure Purview.
Unlike a data warehouse, which typically requires data to be transformed and schema-on-write, a data lake follows a schema-on-read approach. This means the data is stored in its original format without predefined structures. The schema is applied only when the data is being analyzed or processed.

Data Lake on the Cloud: A Modern Foundation for Your Data - AWS, GCP & Azure

RDBMS to centralized Data Lake Architecture
Full support of reading various sources like structured, semi-structured, and unstructured data and ingestion into a centralized Data Lake in cloud storage such as AWS S3, GCP GCS, and Azure Blob Storage using the open table format as Apache Hudi, Apache Iceberg, Delta Lake, or XTable tables.
In today's data-driven world, organizations are trying to store each and every data in raw format as well as in structured format for various kind of analytics, machine learning, data science and business intelligence, including for the feature engineering and data preparation for LLMs and generative AI.
That's why we are here to help you as an expert in this domain to bootstrap or improve the existing data pipeline for a data lake ingestion of your choice of cloud, whether it be AWS, GCP, or Azure.
Our team of certified data engineers and architects specializes in implementing data lakes on cloud platforms like AWS, GCP, and Azure. We provide end-to-end solutions, from data ingestion to processing, storage, and analysis. Our expertise ensures that your data lake is optimized for performance, scalability, and cost-effectiveness. We already have the below-mentioned checklist ready for you to implement the data lake in your organization.
  • Data lake architecture design
  • Defined use cases
  • AL/LLM/ML/BI/Compliance storages
  • Data catalog implementation roadmap
  • Cloud cost management plan
  • Security & access control matrix
  • Security & access control matrix
  • Monitoring and alerting setup(DataOps/CloudWatch)

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