Remove 2014 Remove Data Engineering Remove ETL
article thumbnail

The Full Stack Data Scientist Part 6: Automation with Airflow

Applied Data Science

To keep myself sane, I use Airflow to automate tasks with simple, reusable pieces of code for frequently repeated elements of projects, for example: Web scraping ETL Database management Feature building and data validation And much more! What’s Airflow, and why’s it so good? What makes it my go to?

article thumbnail

Big Data – Lambda or Kappa Architecture?

Data Science Blog

Kappa – Architecture Jay Kreps introduced the Kappa architecture in 2014 as an alternative to the Lambda architecture. It offers the advantage of having a single ETL platform to develop and maintain. It is well-suited for developing data systems that emphasize online learning and do not require a separate batch layer.

Big Data 130
professionals

Sign Up for our Newsletter

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

article thumbnail

How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

article thumbnail

What is the Snowflake Data Cloud and How Much Does it Cost?

phData

Effectively this is a way to store the source of truth and build (or rebuild) your downstream data products (including data warehouses) from it. What is the Difference Between a Data Lake and a Data Warehouse? Historically, there were big differences.