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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. PySpark is the Python API for Apache Spark , which allows Python developers to write Spark applications using Python instead of Scala or Java.

Big Data 161
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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

Greenplum Database is an open-source , hardware-agnostic MPP database for analytics, based on PostgreSQL and developed by Pivotal who was later acquired by VMware. This feature-packed database provides powerful and rapid analytics on data that scales up to petabyte volumes. What Exactly is Greenplum? At a glance – TLDR.

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. The article is based on a research project developed at Grid Dynamics Labs. In addition, we survey the current and emerging technologies and provide a few implementation tips.

Big Data 154
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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. Performance.

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What is a Distributed Storage System

Scalegrid

Key Takeaways Distributed storage systems benefit organizations by enhancing data availability, fault tolerance, and system scalability, leading to cost savings from reduced hardware needs, energy consumption, and personnel. These distributed storage services also play a pivotal role in big data and analytics operations.

Storage 130
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What is IT operations analytics? Extract more data insights from more sources

Dynatrace

Then, big data analytics technologies, such as Hadoop, NoSQL, Spark, or Grail, the Dynatrace data lakehouse technology, interpret this information. Here are the six steps of a typical ITOA process : Define the data infrastructure strategy. Identify data use cases and develop a scalable delivery model with documentation.

Analytics 184
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Driving down the cost of Big-Data analytics - All Things Distributed

All Things Distributed

Werner Vogels weblog on building scalable and robust distributed systems. Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud.

Big Data 111