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Glossary
Data Infrastructure

Apache Spark

Apache Spark is an open-source framework for big data analytics based on the Apache Hadoop platform.

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What are the benefits of Apache Spark?

  • Quick processing: Spark can process data up to 100 times faster than Hadoop MapReduce. This is achieved through the use of in-memory processing and optimized algorithms.
  • Flexibility: Spark supports various data formats, including JSON, CSV, and Parquet. It can also be used with others big data-Frameworks like Hadoop and Hive work together.
  • Variety of functions: Spark offers a wide range of data processing features, including transforms, filtering, aggregation, and machine learning.
  • Scalability: Spark runs on multi-node clusters, allowing it to adapt to growing amounts of data.

How does Apache Spark work?

Spark divides large amounts of data into small blocks On, which as partitions be called. These partitions can then be processed in parallel on multiple nodes of a cluster. The results of parallel processing are then combined.

What are the applications of Apache Spark?

  • Interactive data analysis: Spark can be used to interactively analyze and visualize large amounts of data.
  • Machine learning: Spark offers machine learning features that make it possible to train models from large amounts of data.
  • Stream processing: Spark can be used to process data streams in real time.
  • ETL processes: Spark can be used to extract, transform, and load data into target systems.

What are some examples of using Apache Spark?

  • Analysis of customer behavior: A company can use Spark to analyze their customers' behavior on a website or in an app.
  • Fraud detection: A financial services provider can use Spark to analyze transactions for fraud.
  • Recommendation engines: An online store can use Spark to suggest personalized product recommendations to customers.
  • Predictive maintenance: An industrial company can use Spark to predict the maintenance of their machines.

More information

Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.

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Data Architecture

The right tools in the right place. Update the existing infrastructure or renew most of it.

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Data Strategy

When what happens how and why — that explains the data strategy.

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Machine Learning

Not a gimmick, but real added value

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Data Governance

Not boring or stuffy at all: Aligning ideas and rules

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