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MongoDB CDC: When to Use Kafka, Debezium, Change Streams and Rockset

Rockset

MongoDB has grown from a basic JSON key-value store to one of the most popular NoSQL database solutions in use today. These attributes have caused MongoDB to be widely adopted especially alongside JavaScript web applications. Debezium It is also possible to capture MongoDB change data capture events using Debezium.

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Most important Data Engineering Concepts and Tools for Data Scientists

DareData

Our goal is to help data scientists better manage their models deployments or work more effectively with their data engineering counterparts, ensuring their models are deployed and maintained in a robust and reliable way. DigDag: An open-source orchestrator for data engineering workflows.

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Unstructured Data: Examples, Tools, Techniques, and Best Practices

AltexSoft

Unstructured data , on the other hand, is unpredictable and has no fixed schema, making it more challenging to analyze. Without a fixed schema, the data can vary in structure and organization. A loose schema allows for some data structure flexibility while maintaining a general organization. Hadoop, Apache Spark).

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Data Collection for Machine Learning: Steps, Methods, and Best Practices

AltexSoft

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. They can be accumulated in NoSQL databases like MongoDB or Cassandra.

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15+ Best Data Engineering Tools to Explore in 2023

Knowledge Hut

Strong programming skills: Data engineers should have a good grasp of programming languages like Python, Java, or Scala, which are commonly used in data engineering. Database management: Data engineers should be proficient in storing and managing data and working with different databases, including relational and NoSQL databases.

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Big Data Analytics: How It Works, Tools, and Real-Life Applications

AltexSoft

Big Data analytics encompasses the processes of collecting, processing, filtering/cleansing, and analyzing extensive datasets so that organizations can use them to develop, grow, and produce better products. Big Data analytics processes and tools. Data ingestion. Data storage and processing. NoSQL databases.

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The Good and the Bad of Hadoop Big Data Framework

AltexSoft

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

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