Remove Big Data Remove Google Remove Storage
article thumbnail

Data Storage Formats for Big Data Analytics: Performance and Cost Implications of Parquet, Avro, and ORC

DZone

Efficient data processing is crucial for businesses and organizations that rely on big data analytics to make informed decisions. One key factor that significantly affects the performance of data processing is the storage format of the data.

Big Data 278
article thumbnail

What is Greenplum Database? Intro to the Big Data Database

Scalegrid

High performance, query optimization, open source and polymorphic data storage are the major Greenplum advantages. When handling large amounts of complex data, or big data, chances are that your main machine might start getting crushed by all of the data it has to process in order to produce your analytics results.

Big Data 321
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 252
article thumbnail

What is container orchestration?

Dynatrace

Problems include provisioning and deployment; load balancing; securing interactions between containers; configuration and allocation of resources such as networking and storage; and deprovisioning containers that are no longer needed. Originally created by Google, Kubernetes was donated to the CNCF as an open source project.

article thumbnail

Kubernetes in the wild report 2023

Dynatrace

Most Kubernetes clusters in the cloud (73%) are built on top of managed distributions from the hyperscalers like AWS Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), or Google Kubernetes Engine (GKE). Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch.

article thumbnail

A Recap of the Data Engineering Open Forum at Netflix

The Netflix TechBlog

In this talk, Jessica Larson shares her takeaways from building a new data platform post-GDPR. Creating new development environments is cumbersome: Populating them with data is compute-intensive, and the deployment process is error-prone, leading to higher costs, slower iteration, and unreliable data. Until next time!

article thumbnail

Structural Evolutions in Data

O'Reilly

It progressed from “raw compute and storage” to “reimplementing key services in push-button fashion” to “becoming the backbone of AI work”—all under the umbrella of “renting time and storage on someone else’s computers.” ” (It will be easier to fit in the overhead storage.)

Hardware 135