Ascend.io

FEBRUARY 28, 2024

The rise of generative AI is changing more than just technology; it’s reshaping our professional landscapes — and yes, data engineering is directly experiencing the impact. How does AI recalibrate the workload and priorities of data teams? How can data engineers harness the power of AI?

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Monte Carlo

JULY 27, 2023

These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Data Engineering Podcast

OCTOBER 1, 2023

Summary Artificial intelligence applications require substantial high quality data, which is provided through ETL pipelines. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. With Materialize, you can!

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Building BI SQL Machine Learning 162

Maxime Beauchemin

JANUARY 20, 2017

I joined Facebook in 2011 as a business intelligence engineer. By the time I left in 2013, I was a data engineer. We were data engineers! Data Engineering? Data science as a discipline was going through its adolescence of self-affirming and defining itself. We were pioneers.

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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. A data observability platform, like Monte Carlo’s, can help. It employs machine learning to empower data engineering teams to resolve data issues more rapidly.

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Data Cleanse Data Engineering Data Engineer Engineering 52

Knowledge Hut

JULY 7, 2023

Software engineering, in general, is a dynamic and rapidly changing field that demands a thorough understanding of concepts related to programming, computer science, and mathematics. Working on software engineering research topics is an important part of staying relevant in the field of software engineering.

Software Engineer 52

Software Engineer Software Engineering Engineering Data Mining 52

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

Monte Carlo

MAY 3, 2024

But even though the data landscape is evolving, many enterprise data organizations are still managing data quality the “old” way: with simple data quality monitoring. The basics haven’t changed: high-quality data is still critical to successful business operations.

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High Quality Data Data Architecture Media 52

Monte Carlo

JANUARY 16, 2024

Table of Contents Solve data silos starting at the people-level Keep data governance approachable Oliver Gomes’ data governance best practices Manage and promote the value of high-quality data How will Generative AI impact data quality at Fox? The complexity of a modern data pipeline.

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Government High Quality Data Data Governance Entertainment 52

ProjectPro

FEBRUARY 21, 2023

With so many data engineering certifications available , choosing the right one can be a daunting task. There are over 133K data engineer job openings in the US, but how will you stand out in such a crowded job market? The answer is- by earning professional data engineering certifications! AWS or Azure?

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Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

APRIL 1, 2024

But, for data engineers, there’s something else that comes pretty close to the top of that list: clean data. Data cleaning is an essential step to ensure your data is safe from the adage “garbage in, garbage out.” Implement Routine Data Audits Build a data cleaning cadence into your data teams’ schedule.

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High Quality Data Datasets Data Data Pipeline 52

Monte Carlo

DECEMBER 22, 2023

So, as the year comes to a close, we rounded up our top 5 blogs about generative AI in 2023, to help you catch up before we venture into the AI madness of 2024 These 5 articles aim to give some clarity on the current state of generative AI, how to define use-cases and build for value, and finally, what’s coming next for data and AI teams.

High Quality Data 52

High Quality Data Data Science Data Engineering Data Engineer 52

Monte Carlo

DECEMBER 22, 2023

So, as the year comes to a close, we rounded up our top 5 blogs about generative AI in 2023, to help you catch up before we venture into the AI madness of 2024 These 5 articles aim to give some clarity on the current state of generative AI, how to define use-cases and build for value, and finally, what’s coming next for data and AI teams.

High Quality Data 52

High Quality Data Data Science Data Engineering Data Engineer 52

KDnuggets

MARCH 23, 2023

This article highlights the significance of ensuring high-quality data and presents six key dimensions for measuring it. These dimensions include Completeness, Consistency, Integrity, Timelessness, Uniqueness, and Validity.

High Quality Data 101

High Quality Data Data IT Data Engineering 101

Data Engineering Podcast

JUNE 29, 2020

This was a great conversation about the complexities of working in a niche domain of data analysis and how to build a pipeline of high quality data from collection to analysis. If you hand a book to a new data engineer, what wisdom would you add to it?

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Data Collection Management High Quality Data Metadata 100

Data Engineering Podcast

APRIL 14, 2024

The default association with the term "database" is relational engines, but non-relational engines are also used quite widely. In this episode Oren Eini, CEO and creator of RavenDB, explores the nuances of relational vs. non-relational engines, and the strategies for designing a non-relational database.

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Non-relational Database Relational Database Database Designing 147

Monte Carlo

JANUARY 3, 2024

But RAG development comes with a learning curve, even for your most talented data engineers. They need to know prompt engineering , vector databases and embedding vectors , data modeling, data orchestration , data pipelines and all for RAG. Your data engineering team is the backbone for ensuring data health.

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Technology Database Data Governance Data Engineering 113

Data Engineering Podcast

JULY 16, 2021

This is particularly important in large and complex organizations where domain knowledge and context is paramount and there may not be access to engineers for codifying that expertise. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch.

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High Quality Data Data Engineering Data Engineer Coding 100

DataKitchen

APRIL 11, 2024

DataOps and Its Benefits: The concept of DataOps was introduced, drawing parallels with DevOps in terms of its emphasis on automation, continuous integration, and providing a high-quality data production pipeline. The goal is to reduce errors and operational overhead, allowing data teams to focus on delivering value.

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Manufacturing High Quality Data Data Pipeline Data 60

Precisely

MARCH 14, 2023

Read Quality data you can depend on – today, tomorrow, and beyond For many years Precisely customers have ensured the accuracy of data across their organizations by leveraging our leading data solutions including Trillium Quality, Spectrum Quality, and Data360 DQ+. What does all this mean for your business?

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Data Integration High Quality Data BI Data 52

Pipeline Data Engineering

OCTOBER 15, 2021

Data engineering salon. News and interesting reads about the world of data. Re-evaluating Kafka: issues and alternatives for real-time Olivia Iannone Technical Writer at Estuary Kafka’s challenges have exhausted many an engineer on the path to successful data streaming. What if there was an easier way?

High Quality Data 52

High Quality Data Machine Learning Kafka Data Pipeline 52

Snowflake

MAY 14, 2024

Privacy preservation encompasses a spectrum of techniques, grounded in two fundamental principles: the provision of mathematical assurances of privacy and the prevention of reverse engineering of row-level data and insights. These techniques are frequently employed in collaboration with the use of a data clean room.

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Pharmaceutical IT Hospitality Data 80

Databand.ai

JULY 11, 2023

There are several reasons why organizations need a data quality platform to ensure the accuracy and reliability of their data. With a data quality platform in place, decision-makers can trust the data they use, reducing the risk of costly mistakes and missed opportunities.

Data Cleanse 52

Data Cleanse Telecommunication High Quality Data BI 52

Databand.ai

JULY 17, 2023

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently. Learn more in our detailed guide to DataOps tools (coming soon) What Is MLOps?

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Data Pipeline Machine Learning High Quality Data BI 52

Lyft Engineering

OCTOBER 3, 2023

High-quality data is necessary for the success of every data-driven company. It is now the norm for tech companies to have a well-developed data platform. This makes it easy for engineers to generate, transform, store, and analyze data at the petabyte scale.

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Big Data Metadata Data Warehouse Data 85

Databand.ai

JUNE 14, 2023

The primary goal of data quality testing is to ensure that an organization’s datasets are accurate and reliable enough to support well-informed decisions. Data engineers often utilize specialized tools to perform comprehensive tests on their datasets while tracking metrics related to these aspects throughout the entire lifecycle.

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Amazon Web Services Datasets High Quality Data ETL Tools 52

Data Engineering Podcast

MAY 18, 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. Data lakes are notoriously complex. My thanks to the team at Code Comments for their support.

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Building Data Lake High Quality Data Business Intelligence 278

Databand.ai

MAY 30, 2023

Data quality refers to the degree of accuracy, consistency, completeness, reliability, and relevance of the data collected, stored, and used within an organization or a specific context. High-quality data is essential for making well-informed decisions, performing accurate analyses, and developing effective strategies.

Data Cleanse 85

Data Cleanse Datasets Data Governance Data Validation 85

Monte Carlo

JULY 5, 2023

while overlooking or failing to understand what it really takes to make their tools — and, ultimately, their data initiatives — successful. When it comes to driving impact with your data, you first need to understand and manage that data’s quality. They can really understand [what it means] when data is wrong.”

High Quality Data 52

High Quality Data Data Software Engineer Software Engineering 52

Monte Carlo

OCTOBER 4, 2022

It’s the first book of its kind to help data engineers and analysts understand the critical factors underpinning poor data quality; apply cutting edge technologies to their existing data stacks; and build resilient, observable systems to prevent data downtime from happening in the first place.

High Quality Data 52

High Quality Data Data Pipeline Media Data 52

Monte Carlo

AUGUST 31, 2023

Your downstream data consumers including product analysts, marketing leaders, and sales teams rely on data-driven tools like CRMs, CXPs, CMSs, and any other acronym under the sun to do their jobs quickly and effectively. But what happens when the data is wrong?

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Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Monte Carlo

FEBRUARY 9, 2024

But, when you find a data leader who’s on the real AI journey first-hand (no, not Midjourney), it’s natural to have a few questions. As the Mission Lane data team has continued to invest in their dbt semantic layer , they’ve recognized an opportunity to leverage Snowflake’s Co-Pilot to create a self-service insight engine.

Data 64

Data High Quality Data Architecture Coding 64

Monte Carlo

JANUARY 5, 2024

Data consumers downstream, like data analysts and data scientists, can try new analytical approaches and run their own reports without needing to move or copy data – reducing the workload for data engineers. A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

Data consumers downstream, like data analysts and data scientists, can try new analytical approaches and run their own reports without needing to move or copy data – reducing the workload for data engineers. A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Snowflake

JULY 27, 2023

Once the data is in the warehouse, we are leveraging Snowflake’s data warehousing features to handle it. Something that is especially handy is Snowflake’s support for semi-structured data. This is a great advantage for two reasons.

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Data Architecture Architecture Building Structured Data 52

Monte Carlo

DECEMBER 19, 2022

Data quality testing is the process of validating that key characteristics of a dataset match what is anticipated prior to its consumption. According to Gartner , bad data costs organizations on average an estimated $12.9 Download our Data Quality Testing 101 eBook So, what is data quality testing anyway?

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High Quality Data Data Bytes SQL 52

Data Engineering Weekly

NOVEMBER 11, 2022

I took a break from data engineering and focused on system monitoring and observability for some time. Little did I know I would be building data pipelines to measure system efficiency instead of measuring business efficiency. As the famous saying goes; Once a data engineer, always a data engineer.

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Systems High Quality Data Consulting Metadata 40