Remove Aggregated Data Remove Data Ingestion Remove Hadoop Remove NoSQL
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Sqoop vs. Flume Battle of the Hadoop ETL tools

ProjectPro

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment.

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The Good and the Bad of the Elasticsearch Search and Analytics Engine

AltexSoft

In this edition of “The Good and The Bad” series, we’ll dig deep into Elasticsearch — breaking down its functionalities, advantages, and limitations to help you decide if it’s the right tool for your data-driven aspirations. This means that Elasticsearch can be easily integrated into different modern data stacks.

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20 Best Open Source Big Data Projects to Contribute on GitHub

ProjectPro

DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Apache Spark is also quite versatile, and it can run on a standalone cluster mode or Hadoop YARN , EC2, Mesos, Kubernetes, etc. Presto allows you to query data stored in Hive, Cassandra, relational databases, and even bespoke data storage.

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

AltexSoft

Databases store key information that powers a company’s product, such as user data and product data. The ones that keep only relational data in a tabular format are called SQL or relational database management systems (RDBMSs). Joining: combining data from multiple sources based on a common key or attribute.

IT 59
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Handling Out-of-Order Data in Real-Time Analytics Applications

Rockset

Rockset not only continuously ingests data, but also can “rollup” the data as it is being generated. By using SQL to aggregate data as it is being ingested, this greatly reduces the amount of data stored (5-150x) as well as the amount of compute needed queries (boosting performance 30-100x).