Maxime Beauchemin

JANUARY 20, 2017

The fact that ETL tools evolved to expose graphical interfaces seems like a detour in the history of data processing, and would certainly make for an interesting blog post of its own. Sure, there’s a need to abstract the complexity of data processing, computation and storage.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. For example, grouping the ones about metadata, discoverability, and column naming might have made a lot of sense.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Smart DwH Mover helps in accelerating data warehouse migration.

Data Warehouse 80

Data Warehouse Database-centric Metadata Cloud 80

AltexSoft

JULY 18, 2023

It allows data scientists to analyze large datasets and interactively run jobs on them from the R shell. Big data processing. Distributed: RDDs are distributed across the network, enabling them to be processed in parallel. The details page shows the event timeline. Here are some of the possible use cases.

Big Data 52

Big Data Data Process Process Hadoop 52

Confluent

MAY 9, 2019

Event-first thinking enables us to build a new atomic unit: the event. Four pillars of event streaming. Pillar 1 – Business function: Payment processing pipeline. Pillar 4 – Operational plane: Event logging, DLQs and automation. Journey to Event Driven – Part 2: Programming Models for the Event-Driven Architecture.

Kafka 93

Kafka Pipeline-centric Architecture Database-centric 93

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Obviously, Big Data processing involves hundreds of computing units.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Dataquest

JANUARY 25, 2017

But what about data engineers? A data scientist is only as good as the data they have access to. Most companies store their data in variety of formats across databases and text files. This is where data engineers come in — they build pipelines that transform that data into formats that data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52