The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate. Allegro.io

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

Alation

JANUARY 26, 2023

Governance can — and should — be the responsibility of every data user, though how that’s achieved will depend on the role within the organization. This article will focus on how data engineers can improve their approach to data governance. How can data engineers address these challenges directly?

Data Governance 52

Data Governance Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

FEBRUARY 2, 2023

To get a better grip on those changes we reviewed over 25,000 data scientist job descriptions from that past year to find out what employers are looking for in 2023. Much of what we found was to be expected, though there were definitely a few surprises. You’ll see specific tools in the next section.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

Alation

JANUARY 13, 2022

Thoughtworks says data mesh is key to moving beyond a monolithic data lake. Spoiler alert: data fabric and data mesh are independent design concepts that are, in fact, quite complementary. Thoughtworks says data mesh is key to moving beyond a monolithic data lake 2. Gartner on Data Fabric.

Data Lakes 59

Data Lakes Data Governance Data Quality Data Warehouse 59

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

AI 127

AI AI ML ML 127

Alation

AUGUST 11, 2022

Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Data mesh says architectures should be decentralized because there are inherent problems with centralized architectures. But “customer” is an easy one.

Data Warehouse 52

Data Warehouse Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

NOVEMBER 18, 2024

Introduction to Containers for Data Science/Data Engineering Michael A Fudge | Professor of Practice, MSIS Program Director | Syracuse University’s iSchool In this hands-on session, you’ll learn how to leverage the benefits of containers for DS and data engineering workflows.

Deep Learning 40

Deep Learning Deep Learning Database Data Science 40

DagsHub

OCTOBER 23, 2024

Here are some challenges you might face while managing unstructured data: Storage consumption: Unstructured data can consume a large volume of storage. For instance, if you are working with several high-definition videos, storing them would take a lot of storage space, which could be costly.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Alation

FEBRUARY 17, 2023

Reichental describes data governance as the overarching layer that empowers people to manage data well ; as such, it is focused on roles & responsibilities, policies, definitions, metrics, and the lifecycle of the data. In this way, data governance is the business or process side. Communication is essential.

Data Governance 52

Data Governance Data Quality Data Analyst Data Pipeline 52

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

Data Profiling 52

Data Profiling Data Quality Data Governance Data Pipeline 52

phData

MAY 6, 2024

Now, a single customer might use multiple emails or phone numbers, but matching in this way provides a precise definition that could significantly reduce or even eliminate the risk of accidentally associating the actions of multiple customers with one identity. Store this data in a customer data platform or data lake.

Data Lakes 52

Data Lakes Data Warehouse Cloud Data SQL 52

Mlearning.ai

JULY 10, 2023

Without partitioning, daily data activities will cost your company a fortune and a moment will come where the cost advantage of GCP BigQuery becomes questionable. By keeping the data in cloud storage instead of native BigQuery tables, you can reduce your storage costs while maintaining the ability to query the data.

SQL 52

SQL Database Database Administration Data Lakes 52

The MLOps Blog

JUNE 5, 2023

Stephen: Yeah, absolutely, we’ll definitely delve into that. You have your: feature store model registry data from a data lake The data is then moved across this workflow, modeled and then deployed, Now there’s a good link between your development environments and the production environment where it’s monitoring.

ML 52

ML ML Machine Learning Machine Learning 52

Data Science Blog

MAY 15, 2023

tl;dr Ein Data Lakehouse ist eine moderne Datenarchitektur, die die Vorteile eines Data Lake und eines Data Warehouse kombiniert. Die Definition eines Data Lakehouse Ein Data Lakehouse ist eine moderne Datenspeicher- und -verarbeitungsarchitektur, die die Vorteile von Data Lakes und Data Warehouses vereint.

Data Warehouse 147

Data Warehouse Data Lakes Azure AWS 147

Data Science Blog

MARCH 14, 2023

Von Big Data über Data Science zu AI Einer der Gründe, warum Big Data insbesondere nach der Euphorie wieder aus der Diskussion verschwand, war der Leitspruch “S**t in, s**t out” und die Kernaussage, dass Daten in großen Mengen nicht viel wert seien, wenn die Datenqualität nicht stimme.

Big Data 147

Big Data Big Data Apache Hadoop Data Science 147

AWS Machine Learning Blog

JANUARY 26, 2024

Organizational resiliency draws on and extends the definition of resiliency in the AWS Well-Architected Framework to include and prepare for the ability of an organization to recover from disruptions. With Security Lake, you can get a more complete understanding of your security data across your entire organization.

AWS 138

AWS ML ML AI 138

phData

SEPTEMBER 27, 2024

All this raw data goes into your persistent stage. Then, if you later refine your definition of what constitutes an “engaged” customer, having the raw data in persistent staging allows for easy reprocessing of historical data with the new logic. These changes are streamed into Iceberg tables in your data lake.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

phData

NOVEMBER 1, 2024

Chief Technology Officer, Information Technology Industry Organizations have spent the past decade accumulating, maintaining, and securing data lakes/warehouses/fabrics that will now be expected to drive AI/LLM use cases.

Data Governance 52

Data Governance Data Quality ML ML 52