O'Reilly

FEBRUARY 18, 2020

The results for data-related topics are both predictable and—there’s no other way to put it—confusing. Starting with data engineering, the backbone of all data work (the category includes titles covering data management, i.e., relational databases, Spark, Hadoop, SQL, NoSQL, etc.).

Software Architecture 134

Software Architecture DevOps Data Engineering Architecture 134

Abhishek Tiwari

APRIL 14, 2018

An ETL workflow is responsible for the extraction of data from the source systems, their cleaning, transformation, and loading into the target data warehouse. There are existing formal methods to model the schema of source systems or databases such as entity-relationship diagram (ERD). Closing thoughts.

Best Practices 40

Best Practices Database Efficiency Design 40

Abhishek Tiwari

DECEMBER 22, 2017

In classic Extract, transform, and load (ETL) model, we store entities in their corresponding application databases i.e. as rows in the relational tables. Then we perform frequent batch ETL from application databases to a data warehouse.

Big Data 40

Big Data Retail Storage Google 40

High Scalability

NOVEMBER 12, 2019

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 22

Education Java Software Engineering Engineering 22

High Scalability

OCTOBER 29, 2019

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 22

Education Java Software Engineering Engineering 22

High Scalability

JANUARY 7, 2020

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 21

Education Java Software Engineering Engineering 21

High Scalability

DECEMBER 12, 2019

Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Our intuitive software allows data engineers to maintain pipelines without writing code, and lets analysts gain access to data in minutes instead of months.

Education 21

Education Java Software Engineering Engineering 21

The Netflix TechBlog

DECEMBER 3, 2022

Optionally, this step can use the Write-Audit-Publish pattern to ensure that data is correct before it is made available to the rest of the company. See example below: - template: id: wap type: wap tables: - ${CATALOG}/${DATABASE}/${TABLE} write_jobs: - job: id: write type: Spark spark: script: $S3{./src/sparksql_write.sql}

Best Practices 358

Best Practices Code Testing Data Engineering 358

The Netflix TechBlog

DECEMBER 21, 2020

Some of the optimizations are prerequisites for a high-performance data warehouse. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster. Both automatic (event-driven) as well as manual (ad-hoc) optimization.

Storage 203

Storage Latency Efficiency Data Engineering 203

InfoQ

JULY 14, 2023

Mei-Chin Tsai, Vinod discuss the internal architecture of Azure Cosmos DB and how it achieves high availability, low latency, and scalability. By Mei-Chin Tsai, Vinod Sridharan

Latency 52

Latency Azure Availability Scalability 52

The Netflix TechBlog

MARCH 25, 2019

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Netflix’s diverse data landscape made it challenging to capture all the right data and conforming it to a common data model.

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

O'Reilly

JULY 15, 2020

Technical roles represented in the “Other” category include IT managers, data engineers, DevOps practitioners, data scientists, systems engineers, and systems administrators. Perhaps; we’ll take a look at that next, specifically with respect to containers, centrally managed databases, and monolithic UIs.

Database 135

Database Architecture Education Systems 135

All Things Distributed

OCTOBER 7, 2015

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift.

Cloud 138

Cloud Big Data AWS Analytics 138

Abhishek Tiwari

DECEMBER 27, 2017

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. ETL is a product of the relational database era and it has not evolved much in last decade. There are several emerging data trends that will define the future of ETL in 2018.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

The Netflix TechBlog

OCTOBER 18, 2022

It is a general-purpose workflow orchestrator that provides a fully managed workflow-as-a-service (WAAS) to the data platform at Netflix. It serves thousands of users, including data scientists, data engineers, machine learning engineers, software engineers, content producers, and business analysts, for various use cases.

Java 202

Java Scalability Traffic Architecture 202

All Things Distributed

NOVEMBER 15, 2016

They require teams of data engineers to spend months building complex data models and synthesizing the data before they can generate their first report. Let’s walk through some of the core experiences of QuickSight that make it so easy to set up, connect to your data sources, and build visualizations in minutes.

Analytics 152

Analytics Availability Media Social Media 152