Planning Your Data Architecture for Streaming Analytics
ABSTRACT :
Organizations cannot just buy and turn on streaming analytics at enterprise scale. They need to design the supporting data architecture end to end.
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ABSTRACT :
Organizations cannot just buy and turn on streaming analytics at enterprise scale. They need to design the supporting data architecture end to end.
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Learn the drivers, requirements and best practices for master data management in complex and heterogeneous enterprise environments.
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Learn how to achieve the DataOps objectives of improved efficiency and data quality by migrating to a streaming architecture based on Apache Kafka.
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DataOps helps data engineers and scientists get machine learning right. Improve results with modular development, flexible execution and rigorous testing.
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Many EDW teams now run ETL jobs on Spark in order to improve performance and meet new analytics requirements from the business.
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