Ascend.io

JANUARY 10, 2024

By blending these elements, we lay a solid foundation, ensuring your AI projects don’t just start strong, but also deliver real, lasting value. AI models are only as good as the data they consume, making continuous data readiness crucial. Effective Data Governance: Lastly, let’s talk about governance.

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Data Engineering Podcast

JUNE 5, 2022

In this episode Sean Falconer explains the idea of a data privacy vault and how this new architectural element can drastically reduce the potential for making a mistake with how you manage regulated or personally identifiable information. If you’ve learned something or tried out a project from the show then tell us about it!

Data Security 100

Data Security Metadata MongoDB Scala 100

Data Engineering Podcast

JULY 10, 2022

They also discuss how they have established a guild system for training and supporting data professionals in the organization. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Tired of deploying bad data? Need to automate data pipelines with less red tape?

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

Towards Data Science

JUNE 8, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Why is data quality ownership important? The governance team treats every team output as a data product.

Data Governance 79

Data Governance Government Generalist Data Engineering 79

Data Engineering Podcast

JULY 12, 2021

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Can you start by giving your definition of the modern data stack?

Designing 100

Designing Data Warehouse Data Governance Consulting 100

Monte Carlo

JUNE 6, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Table of Contents Why is important to answer who is responsible for data quality?

Data Governance 98

Data Governance Government Data Data Engineering 98

Data Engineering Podcast

FEBRUARY 24, 2020

Summary Misaligned priorities across business units can lead to tensions that drive members of the organization to build data and analytics projects without the guidance or support of engineering or IT staff. The availability of cloud platforms and managed services makes this a viable option, but can lead to downstream challenges.

IT 100

IT Data Lake Data Pipeline Media 100

Precisely

JANUARY 9, 2023

With global data creation projected to grow to more than 180 zettabytes by 2025 , it’s not surprising that more organizations than ever are looking to harness their ever-growing datasets to drive more confident business decisions.

Data Integration 81

Data Integration Data Governance Government Data 81

Data Engineering Podcast

APRIL 5, 2021

She explains how the design of the platform is informed by the needs of managing data projects for large and small teams across her previous roles, how it integrates with your existing systems, and how it can work to bring everyone onto the same page. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Data Engineering Podcast

OCTOBER 15, 2021

Summary The binding element of all data work is the metadata graph that is generated by all of the workflows that produce the assets used by teams across the organization. The DataHub project was created as a way to bring order to the scale of LinkedIn’s data needs. What are your plans for Data Observability?

Metadata 100

Metadata BI Data Warehouse Government 100

Monte Carlo

JANUARY 10, 2024

Intrinsic data quality is the quality of data assessed independently of its use case. Extrinsic data, meanwhile, is more about the context — it’s how your data interacts with the world outside and how it fits into the larger picture of your project or organization. Data Profiling 2. Data Cleansing 3.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Data Engineering Podcast

APRIL 26, 2021

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.

Business Intelligence 100

Business Intelligence Data Warehouse Hadoop Data Pipeline 100

Ascend.io

SEPTEMBER 5, 2023

In the fast-evolving landscape of cloud data solutions, Snowflake has consistently been at the forefront of innovation, offering enterprises sophisticated tools to optimize their data management. This enhances data governance and aids in decision-making. This paves the way for new interactions and capabilities.

IT 59

IT Scala Java Programming Language 59

Data Engineering Podcast

NOVEMBER 23, 2020

Summary One of the oldest aphorisms about data is "garbage in, garbage out", which is why the current boom in data quality solutions is no surprise. If you hand a book to a new data engineer, what wisdom would you add to it? Interview Introduction How did you get involved in the area of data management?

Hadoop 100

Hadoop Data Pipeline BI Data 100

Data Engineering Podcast

DECEMBER 14, 2020

In this episode Andrew Gross, Bobby Muldoon, and Anup Segu describe the self service data platform that they have built to allow data analysts to own the end-to-end delivery of data projects and how that has allowed them to scale their output. If you hand a book to a new data engineer, what wisdom would you add to it?

Data Analytics 100

Data Analytics Building MySQL Data 100

Monte Carlo

JULY 13, 2023

In this article, we will explore how automation can help data engineering teams overcome these challenges and provide a more seamless and effective data management process. This manual data access process can lead to a bottleneck in the data pipeline , slowing down the process of accessing and utilizing data for decision-making.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Data Engineering Podcast

NOVEMBER 9, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it?

Building 100

Building PostgreSQL BI Metadata 100

Databand.ai

AUGUST 30, 2023

Besides these categories, specialized solutions tailored specifically for particular domains or use cases also exist, such as extract, transform and load (ETL) tools for managing data pipelines, data integration tools for combining information from disparate sources or systems and more.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Data Engineering Podcast

OCTOBER 19, 2020

Summary In order for analytics and machine learning projects to be useful, they require a high degree of data quality. To ensure that your pipelines are healthy you need a way to make them observable. If you hand a book to a new data engineer, what wisdom would you add to it? What is "data downtime"?

Machine Learning 100

Machine Learning Data Engineering Data Engineer Data 100

Data Engineering Podcast

NOVEMBER 30, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it?

Data Integration 100

Data Integration Coding BI Kafka 100

AltexSoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. Bad data management be like, Source: Makeameme Data architects are sometimes confused with other roles inside the data science team.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Edureka

FEBRUARY 7, 2023

An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Data Engineering Podcast

MARCH 31, 2024

To bring observability to dbt projects the team at Elementary embedded themselves into the workflow. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. What are the challenges/shortcomings associated with those approaches?

Project 130

Project Data Lake High Quality Data Data Workflow 130

Knowledge Hut

DECEMBER 26, 2023

Data Engineering Data Engineering involves various operations like creating data pipelines, strategizing data management plans, processing data, and many more. Data Management Data management is an integral part of high-scale organizations.

Data Science 52

Data Science Scala Programming Language R (Programming) 52

Striim

NOVEMBER 8, 2023

However, because most organizations think of data as a by-product, there is little incentive to package and share it with others. For this reason, it is not surprising that 87% of data science projects never make it to production. As a result, implementing data governance in this centralized system can be burdensome.

Architecture 52

Architecture Generalist Government Datasets 52

DataKitchen

JANUARY 5, 2021

The data teams share a common objective; to create analytics for the (internal or external) customer. Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance. Integration with workflow management .

Data Governance 59

Data Governance Government Metadata Data Science 59

Monte Carlo

AUGUST 31, 2023

As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong? This statistic probably comes as no surprise. It certainly didn’t to us.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

ProjectPro

FEBRUARY 21, 2023

Due to the enormous amount of data being generated and used in recent years, there is a high demand for data professionals, such as data engineers, who can perform tasks such as data management, data analysis, data preparation, etc. The rest of the exam details are the same as the DP-900 exam.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Snowflake

AUGUST 16, 2023

What remains challenging is how streaming data is brought together with batch data. That’s why we built Snowpipe Streaming, now generally available to handle row-set data ingestion. Learn more about how to use high-precision geospatial data by watching this virtual hands-on lab.

Scala 52

Scala Transportation Kafka Data Lake 52

Data Engineering Podcast

FEBRUARY 18, 2024

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Multiple open source projects and vendors have been working together to make this vision a reality. Closing Announcements Thank you for listening!

Data Lake 262

Data Lake High Quality Data Data Warehouse Google Cloud 262

Data Engineering Podcast

APRIL 21, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. If you've learned something or tried out a project from the show then tell us about it!

Data Lake 182

Data Lake High Quality Data Data Pipeline Machine Learning 182

Data Engineering Podcast

NOVEMBER 19, 2023

Summary The dbt project has become overwhelmingly popular across analytics and data engineering teams. Dustin Dorsey and Cameron Cyr co-authored a practical guide to building your dbt project. In this episode they share their hard-won wisdom about how to build and scale your dbt projects.

Project 147

Project Data Lake High Quality Data SQL 147

Data Engineering Podcast

MARCH 24, 2024

Summary A core differentiator of Dagster in the ecosystem of data orchestration is their focus on software defined assets as a means of building declarative workflows. With their launch of Dagster+ as the redesigned commercial companion to the open source project they are investing in that capability with a suite of new features.

Data Lake 162

Data Lake High Quality Data Hadoop Data Pipeline 162

Data Engineering Podcast

APRIL 14, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents data quality issues from entering every part of your data workflow, from migration to dbt deployment.

Non-relational Database 147

Non-relational Database Relational Database Database Designing 147

Data Engineering Podcast

MAY 5, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. If you've learned something or tried out a project from the show then tell us about it! Links Aigo.ai

Building 147

Building Data Lake High Quality Data Machine Learning 147

Data Engineering Podcast

MAY 12, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. If you've learned something or tried out a project from the show then tell us about it! Want to see Starburst in action?

Management 130

Management Data Lake High Quality Data Machine Learning 130

Data Engineering Podcast

FEBRUARY 4, 2024

In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex.

SQL 173

SQL Data Lake High Quality Data Data Pipeline 173