dbt Developer Hub

APRIL 24, 2023

Alteryx is a visual data transformation platform with a user-friendly interface and drag-and-drop tools. Nonetheless, Alteryx may have difficulties to cope with the complexity increase within an organization’s data pipeline, and it can become a suboptimal tool when companies start dealing with large and complex data transformations.

BI 83

BI Data Workflow SQL Data Pipeline 83

Grouparoo

OCTOBER 6, 2021

Themes I was drawn to the articles that speak to a theme in the data world that I am passionate about: how data pipelines and data team practices are evolving to be more like traditional product development. 7 Be Intentional About the Batching Model in Your Data Pipelines Different batching models.

Data Engineer 52

Data Engineer Data Engineering Engineering Pipeline-centric 52

Databand.ai

NOVEMBER 4, 2022

Re-Imagining Data Observability Ryan Yackel 2022-11-04 10:36:35 Data observability has become one of the hottest topics of the year – and for good reason. Data observability provides an end-to-end view into exactly what’s happening with data pipelines across an organization’s data fabric.

Data 52

Data Data Pipeline Retail Metadata 52

Data Engineering Weekly

OCTOBER 30, 2022

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Sign up free to test out the tool today.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Ingestion 52

Airbnb Tech

NOVEMBER 28, 2023

By: Clark Wright Introduction These days, as the volume of data collected by companies grows exponentially, we’re all realizing that more data is not always better. In fact, more data, especially if you can’t rely on its quality, can hinder a company by slowing down decision-making or causing poor decisions.

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

Rockset

FEBRUARY 24, 2023

An instructive example is clickstream data, which records a user’s interactions on a website. Another example would be sensor data collected in an industrial setting. The common thread across these examples is that a large amount of data is being generated in real time. This is the single most popular streaming platform.

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

ProjectPro

NOVEMBER 30, 2021

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process 52

Process Data Pipeline Data Warehouse AWS 52

Grouparoo

NOVEMBER 29, 2021

For example, service agreements may cover data quality, latency, and availability, but they are outside the organization's control. Primary Data Sources are those where data collection is from its point of creation before any processing. It may be raw data, validated data, or big data.

Raw Data 40

Raw Data Big Data Relational Database Data Warehouse 40

AltexSoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering. How to get started with data virtualization.

Process 69

Process Data Lake Metadata Data Warehouse 69

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 Relational Database AWS 52

Ascend.io

OCTOBER 12, 2023

Provide Continuous Support: As the team adapts to automated data pipelines , be ready to offer support. Schedule Regular Audits: Even post-automation, periodically review your data for quality and relevance. Inconsistent, outdated, or inaccurate data can compromise the results of your automation efforts.

Hospitality 52

Hospitality Data Pipeline Healthcare Data Governance 52

Monte Carlo

OCTOBER 5, 2023

Data freshness (aka data timeliness) means your data should be up-to-date and relevant to the timeframe of analysis. Data validity means your data conforms to the required format, type, or range of values. Example: Email addresses in the customer database should match a valid format (e.g.,

Government 52

Government Data Governance Data Datasets 52

DataKitchen

JANUARY 23, 2023

It allows organizations to see how data is being used, where it is coming from, its quality, and how it is being transformed. DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. What is missing in data lineage?

Data Governance 52

Data Governance Government Data Pipeline Data 52