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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.

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Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

With the rapid growth of technology, more and more data volume is coming in many different formats—structured, semi-structured, and unstructured. Data analytics on operational data at near-real time is becoming a common need. Then we can query the data with Amazon Athena visualize it in Amazon QuickSight.

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Access Amazon Athena in your applications using the WebSocket API

AWS Big Data

Many organizations are building data lakes to store and analyze large volumes of structured, semi-structured, and unstructured data. In addition, many teams are moving towards a data mesh architecture, which requires them to expose their data sets as easily consumable data products. Install NPM.

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

Simply put, data visualization means showing data in a visual format that makes insights easier to understand for human users. Data is usually visualized in a pictorial or graphical form such as charts, graphs, lists, maps, and comprehensive dashboards that combine these multiple formats.

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Celebrating Data Superheroes: The 2021 Data Impact Awards Winners

Cloudera

So, without further ado, it is with great delight that we officially publish the 2021 Data Impact Award winners! Data Lifecycle Connection. This allows for an omni-channel view of the customer and enables real-time data streaming and a safe zone to test machine learning models using Cloudera Data Science Workbench (CDSW).

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What is a Data Pipeline?

Jet Global

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.