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

Ascend.io

JANUARY 10, 2024

AI models are only as good as the data they consume, making continuous data readiness crucial. Here are the key processes that need to be in place to guarantee consistently high-quality data for AI models: Data Availability: Establish a process to regularly check on data availability. Actionable tip?

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Databand.ai

JULY 10, 2023

An observability platform is a comprehensive solution that allows data engineers to monitor, analyze, and optimize their data pipelines. By providing a holistic view of the data pipeline, observability platforms help teams rapidly identify and address issues or bottlenecks.

Data Pipeline 52

Data Pipeline Algorithm Raw Data Aggregated Data 52

Monte Carlo

JANUARY 10, 2024

On the other hand, “Can the marketing team easily segment the customer data for targeted communications?” usability) would be about extrinsic data quality. You might discover, for example, that a particular data source is consistently producing errors, indicating a need for better data collection methods.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Monte Carlo

MARCH 24, 2023

The key differences are that data integrity refers to having complete and consistent data, while data validity refers to correctness and real-world meaning – validity requires integrity but integrity alone does not guarantee validity. What is Data Integrity? How Do You Maintain Data Integrity?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Monte Carlo

MAY 25, 2023

Picture this: your data is scattered. Data pipelines originate in multiple places and terminate in various silos across your organization. Your data is inconsistent, ungoverned, inaccessible, and difficult to use. Some of the value companies can generate from data orchestration tools include: Faster time-to-insights.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Monte Carlo

JULY 11, 2023

In other words, is it likely your data is accurate based on your expectations? Data collection methods: Understand the methodology used to collect the data. Look for potential biases, flaws, or limitations in the data collection process.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40