Data Engineering Podcast

APRIL 28, 2024

In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that developers already use. Data lakes are notoriously complex. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!

Building 147

Building Data Lake High Quality Data Machine Learning 147

Cloudera

DECEMBER 16, 2022

Cloudera has long had the capabilities of a data lakehouse, if not the label. Cloudera enables an open data lakehouse architecture that combines all the flexibility of the data lake with the performance of the data warehouse, so enterprises can use all data — both structured and unstructured.

Database 90

Database Cloud Systems Management 90

Ascend.io

JANUARY 2, 2024

The goal is to cleanse, merge, and optimize the data, preparing it for insightful analysis and informed decision-making. Destination and Data Sharing The final component of the data pipeline involves its destinations – the points where processed data is made available for analysis and utilization.

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

U-Next

SEPTEMBER 23, 2022

The fast development of digital technologies, IoT goods and connectivity platforms, social networking apps, video, audio, and geolocation services has created the potential for massive amounts of data to be collected/accumulated. The process of preparing data for analysis is known as extract, transform, and load (ETL).

Big Data Ecosystem 40

Big Data Ecosystem Big Data Healthcare Data Lake 40

U-Next

JUNE 29, 2022

Preparing data for analysis is known as extract, transform and load (ETL). While the ETL workflow is becoming obsolete, it still serves as a common word for the data preparation layers in a big data ecosystem. Working with large amounts of data necessitates more preparation than working with less data.

Big Data 40

Big Data Big Data Ecosystem Data Lake Raw Data 40

Rockset

DECEMBER 9, 2019

As a data engineer, my time is spent either moving data from one place to another, or preparing it for exposure to either reporting tools or front end users. As data collection and usage have become more sophisticated, the sources of data have become a lot more varied and disparate, volumes have grown and velocity has increased.

Data Engineering 40

Data Engineering Data Engineer Raw Data Engineering 40

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. It ensures that the data collected from cloud sources or local databases is complete and accurate.

Big Data 52

Big Data Hadoop AWS Relational Database 52