Knowledge Hut

MARCH 15, 2024

The job description for Azure data engineer that I have elucidated below focuses more on foundational tasks while providing opportunities for learning and growth within the field: Data ingestion: This role involves assisting in the process of collecting and importing data from various sources into Azure storage solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Christophe Blefari

APRIL 12, 2024

In the same conference a lot of other talks took place here a few selection you should check out: How to pivot your data team from a service team to a value-generator — Very often data teams struggle in delivering value or in finding what's their real identity. Local pipeline development with SQLMesh, Airflow, and Postgres.

BI 130

BI SQL Data Coding 130

François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. How do we build data products ? How can we interoperate between the data domains ? We want interoperability for any data stored versus we have to think how to store the data in a specific node to optimize the processing.

Technology 130

Technology Architecture Google Cloud Metadata 130

Data Engineering Weekly

MARCH 17, 2024

[link] Jarkko Moilanen: Exploring the Frontier of Data Products - Seeking Insights on Emerging Standards Data products are becoming a central part of the data pipeline strategy, bringing a new approach to building data as an asset.

Data Engineering 70

Data Engineering Data Engineer Engineering Data Governance 70

Data Engineering Weekly

MARCH 3, 2024

These improvements, part of Project Lightspeed, aim to reduce latency and increase throughput for stateful streaming queries, making them more efficient and cost-effective. The article also highlights sink-specific improvements and operator-specific enhancements that contribute to the overall performance boost.

Data Engineering 67

Data Engineering Data Engineer Pipeline-centric Engineering 67

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. This process ensures your data is accurate, complete, and consistently up-to-date — preventing any degradation that could mislead your AI, which can lead to erroneous outputs. And technology?

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Precisely

MAY 13, 2024

It synthesizes all the metadata around your organization’s data assets and arranges the information into a simple, easy-to-understand format. Questions to ask each vendor: Does your data integration solution provide access to the metadata? What data governance controls do your solutions have in place?

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

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 75

Data Governance Government Generalist Data Engineering 75

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

Databand.ai

AUGUST 30, 2023

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

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

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

JUNE 5, 2022

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.

Data Security 100

Data Security Metadata MongoDB Scala 100

Data Engineering Podcast

JULY 10, 2022

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. Tired of deploying bad data?

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

Ascend.io

OCTOBER 12, 2023

Data professionals often find themselves entangled in repetitive and time-consuming tasks. With the right automation tool, these routine operations can be efficiently handled, allowing professionals to focus on projects that truly matter. Regulatory Compliance: Be aware of any data-related regulations in your industry.

Hospitality 52

Hospitality Data Pipeline Healthcare Data Governance 52

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. RudderStack’s smart customer data pipeline is warehouse-first.

Designing 100

Designing Data Warehouse Data Governance Consulting 100

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. Are you spending too much time maintaining your data pipeline?

IT 100

IT Data Lake Data Pipeline Media 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

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

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

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

JULY 13, 2023

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 engineers end up spending 30-50% of their time granting and revoking access, instead of working on their core projects. Image courtesy of Satori.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 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

Knowledge Hut

SEPTEMBER 21, 2023

Azure Data Engineer: Overview Being an Azure Data Engineer is an exhilarating journey filled with opportunities for growth and innovation and making a real impact in the world of data. The role of an Azure data engineer revolves around designing, building, and maintaining data pipelines and infrastructure on the Azure platform.

Data Engineering 52

Data Engineering Data Engineer Engineering Cloud Computing 52

Ascend.io

SEPTEMBER 5, 2023

Data Governance: With Ascend, users can track various metrics such as byte consumption and record counts, as well as build lineage through the entire process, including Python and SQL. 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. I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help.

Hadoop 100

Hadoop Data Pipeline BI Data Engineering 100

DareData

JULY 5, 2023

From this point on, two things may happen: either the analytics team goes forward but will not be able to deploy their findings or they will discard the usage of the data from the project. The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

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. Can you describe how your data platform is implemented?

Data Analytics 100

Data Analytics Building MySQL Data 100

Data Engineering Podcast

NOVEMBER 9, 2020

I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. 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.

Building 100

Building PostgreSQL BI Metadata 100

Monte Carlo

JULY 27, 2023

Data quality engineers also need to have experience operating in cloud environments and using many of the modern data stack tools that are utilized in building and maintaining data pipelines. 78% of job postings referenced at least part of their environment was in a modern data warehouse, lake, or lakehouse.

Engineering 52

Engineering Healthcare Scala Data Warehouse 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. Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud.

Machine Learning 100

Machine Learning Data Engineering Data Engineer Data 100

Monte Carlo

MAY 18, 2023

Whether you’re a data scientist, data engineer, or business analyst, keeping track of your data’s origin, transformation, and movement is crucial for maintaining transparency, enforcing data governance, and ensuring data quality. Why do you need data lineage?

Metadata 64

Metadata Data Lake Government Data Governance 64

Data Engineering Podcast

NOVEMBER 30, 2020

I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. 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.

Data Integration 100

Data Integration Coding BI Kafka 100

RandomTrees

FEBRUARY 29, 2024

Assess Current Infrastructure Costs: The most vital phase in any migration project is surveying current infrastructure costs. Snowflake cost management includes assessing hardware, maintenance, software licenses, and faculty costs for managing existing data warehouses.

Data Warehouse 52

Data Warehouse Utilities Consulting Certification 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

Data Engineering Podcast

MARCH 31, 2024

To bring observability to dbt projects the team at Elementary embedded themselves into the workflow. Dagster offers a new approach to building and running data platforms and data pipelines. Can you start by outlining what elements of observability are most relevant for dbt projects?

Project 130

Project Data Lake High Quality Data Data Workflow 130

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Data governance and management hurdles Poor data governance practices can jeopardize the accuracy of entire datasets.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40