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Materialized Views in Hive for Iceberg Table Format

Cloudera

Materialized views are valuable for accelerating common classes of business intelligence (BI) queries that consist of joins, group-bys and aggregate functions. The snapshotId of the source tables involved in the materialized view are also maintained in the metadata. Furthermore, it is partitioned on the d_year column.

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Turning Streams Into Data Products

Cloudera

This blog aims to answer two questions as illustrated in the diagram below: How have stream processing requirements and use cases evolved as more organizations shift to “streaming first” architectures and attempt to build streaming analytics pipelines? Meet Laila, a very opinionated practitioner of Cloudera Stream Processing.

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What is Data Hub: Purpose, Architecture Patterns, and Existing Solutions Overview

AltexSoft

The main purpose of a DW is to enable analytics: It is designed to source raw historical data, apply transformations, and store it in a structured format. This type of storage is a standard part of any business intelligence (BI) system, an analytical interface where users can query data to make business decisions.

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Building a Self-Managed Shared Data Experience

Cloudera

That data may be hard to discover for other users and other applications. Worse, the metadata and context associated with that data may be lost forever if a transient cluster is shut down and the resources released. A way to leverage the benefits of cloud for multi-disciplinary analytics, without all of those problems.

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Tableau Tutorial

U-Next

Tableau serves as a visual framework for business intelligence and analytics, assisting users in watching, observing, comprehending, and making choices with various data types. A rapidly expanding data visualization tool called Tableau Software is creating a stir within Business Intelligence (BI) sector.

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The Good and the Bad of Apache Kafka Streaming Platform

AltexSoft

A subscriber is a receiving program such as an end-user app or business intelligence tool. The tool takes care of storing metadata about partitions and brokers. Hadoop fits heavy, not time-critical analytics applications that generate insights for long-term planning and strategic decisions. ZooKeeper issue.

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The Good and the Bad of Apache Spark Big Data Processing

AltexSoft

It has in-memory computing capabilities to deliver speed, a generalized execution model to support various applications, and Java, Scala, Python, and R APIs. Spark Streaming enhances the core engine of Apache Spark by providing near-real-time processing capabilities, which are essential for developing streaming analytics applications.