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Handling Bursty Traffic in Real-Time Analytics Applications

Rockset

We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them! Lambda systems try to accommodate the needs of both big data-focused data scientists as well as streaming-focused developers by separating data ingestion into two layers.

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Azure Databricks: A Comprehensive Guide

Analytics Vidhya

Introduction Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform that is built on top of the Microsoft Azure cloud. A collaborative and interactive workspace allows users to perform big data processing and machine learning tasks easily.

Big Data 310
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5 Apache Spark Best Practices

Data Science Blog: Data Engineering

Introduction Spark’s aim is to create a new framework that was optimized for quick iterative processing, such as machine learning and interactive data analysis while retaining Hadoop MapReduce’s scalability and fault-tolerant. This could handle packet and real-time data processing and predictive analysis workloads.

Hadoop 52
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Top 8 Data Engineering Books [Beginners to Advanced]

Knowledge Hut

This article suggests the top eight data engineer books ranging from beginner-friendly manuals to in-depth technical references. What is Data Engineering? It refers to a series of operations to convert raw data into a format suitable for analysis, reporting, and machine learning which you can learn from data engineer books.

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Discover and Explore Data Faster with the CDP DDE Template

Cloudera

DDE is a new template flavor within CDP Data Hub in Cloudera’s public cloud deployment option (CDP PC). It is designed to simplify deployment, configuration, and serviceability of Solr-based analytics applications. data best served through Apache Solr). data best served through Apache Solr). What does DDE entail?

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SQL and Complex Queries Are Needed for Real-Time Analytics

Rockset

We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them! The tradeoff of these first-generation SQL-based big data systems was that they boosted data processing throughput at the expense of higher query latency.

SQL 52
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Data Mesh Architecture: Revolutionizing Event Streaming with Striim

Striim

What are the four principles of a Data Mesh, and what problems do they solve? A data mesh is technology-agnostic and underpins four main principles described in-depth in this blog post by Zhamak Dehghani. This information can then be used to improve customer experiences or develop more efficient business processes.