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Cutting Big Data Costs: Effective Data Processing With Apache Spark

DZone

In today's data-driven world, efficient data processing plays a pivotal role in the success of any project. Apache Spark , a robust open-source data processing framework, has emerged as a game-changer in this domain.

Big Data 279
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Write Optimized Spark Code for Big Data Applications

DZone

Apache Spark is a powerful open-source distributed computing framework that provides a variety of APIs to support big data processing. In this article, we will discuss some tips and techniques for tuning PySpark applications.

Big Data 173
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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

Greenplum Database is a massively parallel processing (MPP) SQL database that is built and based on PostgreSQL. It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data.

Big Data 321
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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications. Fault-tolerance.

Big Data 154
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ScyllaDB Trends – How Users Deploy The Real-Time Big Data Database

Scalegrid

ScyllaDB offers significantly lower latency which allows you to process a high volume of data with minimal delay. There are dozens of quality articles on ScyllaDB vs. Cassandra, so we’ll stop short here so we can get to the real purpose of this article, breaking down the ScyllaDB user data.

Big Data 187
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Kubernetes for Big Data Workloads

Abhishek Tiwari

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Key challenges.

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An overview of end-to-end entity resolution for big data

The Morning Paper

An overview of end-to-end entity resolution for big data , Christophides et al., 2020, Article No. It’s an important part of many modern data workflows, and an area I’ve been wrestling with in one of my own projects. The processing mode – traditional batch (with or without budget constraints), or incremental.