Remove Aggregated Data Remove MongoDB Remove Structured Data
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Data Lake vs. Data Warehouse: Differences and Similarities

U-Next

Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. Gen 2 Azure Data Lake Storage . Data lakes can also be organized and queried using other technologies, such as . Atlas Data Lake powered by MongoDB. .

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Most important Data Engineering Concepts and Tools for Data Scientists

DareData

Examples of NoSQL databases include MongoDB or Cassandra. Data lakes: These are large-scale data storage systems that are designed to store and process large amounts of raw, unstructured data. Examples of technologies able to aggregate data in data lake format include Amazon S3 or Azure Data Lake.

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What is a Data Pipeline (and 7 Must-Have Features of Modern Data Pipelines)

Striim

Striim supported American Airlines by implementing a comprehensive data pipeline solution to modernize and accelerate operations. To achieve this, the TechOps team implemented a real-time data hub using MongoDB, Striim, Azure, and Databricks to maintain seamless, large-scale operations.

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Comparing ClickHouse vs Rockset for Event and CDC Streams

Rockset

This means users need to configure their streams to batch data ahead of loading into ClickHouse. Rockset has native connectors that ingest event streams from Kafka and Kinesis and CDC streams from databases like MongoDB, DynamoDB, Postgres and MySQL. ClickHouse has several storage engines that can pre-aggregate data.

MySQL 52
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Sqoop vs. Flume Battle of the Hadoop ETL tools

ProjectPro

Hadoop Sqoop and Hadoop Flume are the two tools in Hadoop which is used to gather data from different sources and load them into HDFS. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., Sqoop does not support importing of data from non-RDBMS such as MongoDB and Cassandra.

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The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

AltexSoft

Also, there are NoSQL databases that can be home to all sorts of data, including unstructured and semi-structured (images, PDF files, audio, JSON, etc.) Some popular databases are Postgres and MongoDB. But this distinction has been blurred with the era of cloud data warehouses.

IT 59
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Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. Step 1- Automating the Lakehouse's data intake.