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Empowering Developers With Query Flexibility

Rockset

It’s difficult to create data analytics systems that can easily do this while maintaining fast query performance and real-time capabilities. It’s even harder to do this without constantly updating your data ops in some way. What databases are you using for real-time analytics?

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Rockset Is Up to 9.4x Faster than Apache Druid on the Star Schema Benchmark

Rockset

As it usually gets less focus in benchmarks, we released RockBench , a data latency benchmark, last September. Query Latency and the Star Schema Benchmark Query latency is the second key measure of real-time analytics performance and is the focus of the rest of this post. Rockset was 9.4x

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Analytics-on-the-fly: from batch to real-time user engagement

Rockset

No more batch analytics.this is analytics-on-the-fly! The challenge of building analytical applications on your most recent datasets is a tough challenge. Firstly, if you have to make instantaneous decisions on recent data, you do not have time to clean it or sanitize it before processing. Why is that?

Hadoop 52
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Why Real-Time Analytics Requires Both the Flexibility of NoSQL and Strict Schemas of SQL Systems

Rockset

Take the Hive analytics database that is part of the Hadoop stack. When it encounters semi-structured data that does not fit neatly into its existing tables and databases, it simply stores the data as a JSON-like blob. This keeps the data intact. Hive does support flexible schemas, but crudely.

NoSQL 52
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SQL for Data Engineering: Success Blueprint for Data Engineers

ProjectPro

To analyze big data and create data lakes and data warehouses , SQL-on-Hadoop engines run on top of distributed file systems. The SQL-on-Hadoop platform combines the Hadoop data architecture with traditional SQL-style structured data querying to create a specific analytical application tool.

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Top 6 Big Data and Business Analytics Companies to Work For in 2023

ProjectPro

The company targets to deliver values to its customers through the free SaaS based analytics applications so that it can build credibility with the clients to encourage them to buy more. Palantir Metropolis- This product focusses on information management, data integration and quantitative analytics.

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A Flexible and Efficient Storage System for Diverse Workloads

Cloudera

Today’s platform owners, business owners, data developers, analysts, and engineers create new apps on the Cloudera Data Platform and they must decide where and how to store that data. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases.

Systems 87