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Top Data Lakes Interview Questions

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction A data lake is a centralized repository for storing, processing, and securing massive amounts of structured, semi-structured, and unstructured data. It can store data in its native format and process any type of data, regardless of size.

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Key Components and Challenges of Data Lakes

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Today, Data Lake is most commonly used to describe an ecosystem of IT tools and processes (infrastructure as a service, software as a service, etc.) that work together to make processing and storing large volumes of data easy.

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A Detailed Introduction on Data Lakes and Delta Lakes

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction A data lake is a central data repository that allows us to store all of our structured and unstructured data on a large scale. The post A Detailed Introduction on Data Lakes and Delta Lakes appeared first on Analytics Vidhya.

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Setting up Data Lake on GCP using Cloud Storage and BigQuery

Analytics Vidhya

Introduction A data lake is a centralized and scalable repository storing structured and unstructured data. The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

licensed, 100% open-source data table format that helps simplify data processing on large datasets stored in data lakes. Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time.

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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. Data Type and Processing.

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Monitor data pipelines in a serverless data lake

AWS Big Data

The combination of a data lake in a serverless paradigm brings significant cost and performance benefits. By monitoring application logs, you can gain insights into job execution, troubleshoot issues promptly to ensure the overall health and reliability of data pipelines.