Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

Tableau

SEPTEMBER 23, 2021

If we asked you, “What does your organization need to help more employees be data-driven?” where would “better data governance” land on your list? We’re all trying to use more data to make decisions, but constantly face roadblocks and trust issues related to data governance. . A data governance framework.

Data Governance 102

Data Governance Analytics Analytics Tableau 102

Tableau

SEPTEMBER 23, 2021

If we asked you, “What does your organization need to help more employees be data-driven?” where would “better data governance” land on your list? We’re all trying to use more data to make decisions, but constantly face roadblocks and trust issues related to data governance. . A data governance framework.

Data Governance 97

Data Governance Analytics Analytics Tableau 97

Alation

SEPTEMBER 7, 2021

In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. But governance is a time-consuming process (for users and data stewards alike).

Data Governance 59

Data Governance Data Scientist Data Quality Data Profiling 59

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

NOVEMBER 13, 2024

This required custom integration efforts, along with complex AWS Identity and Access Management (IAM) policy management, further complicating the model governance process. With the integration of SageMaker and Amazon DataZone, it enables collaboration between ML builders and data engineers for building ML use cases.

ML 120

ML ML AWS Data Preparation 120

ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

IBM Journey to AI blog

FEBRUARY 26, 2024

The financial services industry has been in the process of modernizing its data governance for more than a decade. But as we inch closer to global economic downturn, the need for top-notch governance has become increasingly urgent. Trust and data governance Data governance isn’t new, especially in the financial world.

Data Pipeline 112

Data Pipeline Data Engineering Data Engineering Data Engineering 112

AWS Machine Learning Blog

OCTOBER 20, 2023

Data and governance foundations – This function uses a data mesh architecture for setting up and operating the data lake, central feature store, and data governance foundations to enable fine-grained data access.

ML 137

ML ML AWS Data Lakes 137

The Data Administration Newsletter

OCTOBER 17, 2023

Where exactly within an organization does the primary responsibility lie for ensuring that a data pipeline project generates data of high quality, and who exactly holds that responsibility? Who is accountable for ensuring that the data is accurate? Is it the data engineers? The data scientists?

Data Pipeline 52

Data Pipeline Data Quality Data Governance Data Analyst 52

Data Science Dojo

OCTOBER 10, 2023

The speaker is Andrew Madson, a data analytics leader and educator. The event is for anyone interested in learning about generative AI and data storytelling, including business leaders, data scientists, and enthusiasts. The event will be conducted online, making it accessible to a global audience.

AI 243

AI AI Data Observability Artificial Intelligence 243

Dataversity

AUGUST 10, 2023

To get to the bottom of these questions and more, we conducted a survey of 100 survey respondents, at least 63 […] The post Which Data Quality Issues Are Plaguing Data Engineers Today? appeared first on DATAVERSITY.

Data Quality 52

Data Quality Data Engineering Data Engineer Data Engineering 52

Smart Data Collective

OCTOBER 17, 2022

Engineering teams, in particular, can quickly get overwhelmed by the abundance of information pertaining to competition data, new product and service releases, market developments, and industry trends, resulting in information anxiety. Explosive data growth can be too much to handle. Data pipeline maintenance.

Big Data 119

Big Data Big Data Data Engineering Data Engineering 119

AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

AWS 134

AWS ML ML Machine Learning 134

AWS Machine Learning Blog

APRIL 26, 2023

The first is by using low-code or no-code ML services such as Amazon SageMaker Canvas , Amazon SageMaker Data Wrangler , Amazon SageMaker Autopilot , and Amazon SageMaker JumpStart to help data analysts prepare data, build models, and generate predictions. Monitoring setup (model, data drift).

ML 123

ML ML AWS Machine Learning 123

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. For example, neptune.ai Check out the Kubeflow documentation.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. Invest in strong data management and governance up front—it pays off downstream.

AI 98

AI AI Tableau Data Scientist 98

phData

FEBRUARY 16, 2023

An ACE is a dedicated team or unit within an organization that is responsible for managing and optimizing the use of data and analytics. They will be responsible for leading data-driven projects and initiatives–and for communicating the insights and recommendations derived from data analysis to stakeholders.

Analytics 52

Analytics Analytics Data Analyst Data Governance 52

Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. Invest in strong data management and governance up front—it pays off downstream.

AI 95

AI AI Tableau Data Scientist 95

Alation

JUNE 21, 2022

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that. ML and DataOps teams).

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

Alation

JUNE 21, 2023

This enables faster migration, provides visibility, fosters collaboration across data teams to ensure frictionless lakehouse adoption, and facilitates self-service analytics. As a result, data engineers and data scientists can reinforce data as a product across their organizations.”

Data Governance 52

Data Governance Data Scientist Data Engineering Data Engineering 52

ODSC - Open Data Science

FEBRUARY 5, 2025

This May, were heading to Boston for ODSC East 2025, where data scientists, AI engineers, and industry leaders will gather to explore the latest advancements in AI, machine learning, and data engineering. The wait is almost over! This is your chance to gain insights from some of the brightest minds in the industry.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

AUGUST 12, 2024

Consequently, AIOps is designed to harness data and insight generation capabilities to help organizations manage increasingly complex IT stacks. MLOps platforms are primarily used by data scientists, ML engineers, DevOps teams and ITOps personnel who use them to automate and optimize ML models and get value from AI initiatives faster.

Big Data 106

Big Data Big Data ML ML 106

AWS Machine Learning Blog

FEBRUARY 7, 2025

In this post, we assign the functions in terms of the ML lifecycle to each role as follows: Lead data scientist Provision accounts for ML development teams, govern access to the accounts and resources, and promote standardized model development and approval process to eliminate repeated engineering effort.

ML 70

ML ML Data Scientist AWS 70

ODSC - Open Data Science

APRIL 26, 2023

Though just about every industry imaginable utilizes the skills of a data-focused professional, each has its own challenges, needs, and desired outcomes. This is why you’ll often find that there are jobs in AI specific to an industry, or desired outcome when it comes to data.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

phData

MAY 28, 2024

With Cortex, business analysts, data engineers, and developers can easily incorporate Predictive and Generative AI into their workflows using simple SQL commands and intuitive interfaces. As an example, check out our blog post on implementing a data engineering pipeline for Sentiment Analysis with dbt. What is Snowpark?

Machine Learning 52

Machine Learning Machine Learning Data Engineering Data Engineering 52

IBM Journey to AI blog

DECEMBER 16, 2022

And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.

Data Quality 52

Data Quality Data Governance Data Scientist Data Wrangling 52

Precisely

AUGUST 15, 2024

Key Players in AI Development Enterprises increasingly rely on AI to automate and enhance their data engineering workflows, making data more ready for building, training, and deploying AI applications. Data engineers focus on tasks like cleansing and managing data, ensuring its quality and readiness for AI applications.

AI 64

AI AI Data Quality Data Engineering 64

Alation

JUNE 7, 2022

Fifth Third faced a number of pain points borne of a large data landscape. The Problem: The Data Challenges. The data challenges at Fifth Third will sound familiar to anyone working in an enterprise data landscape. To meet that growing demand, they decided to make everyone a data citizen.

Data Governance 52

Data Governance Data Scientist Data Engineering Data Engineering 52

Alation

AUGUST 16, 2022

This week, IDC released its second IDC MarketScape for Data Catalogs report, and we’re excited to share that Alation was recognized as a leader for the second consecutive time. These include data analysts, stewards, business users , and data engineers. Leader in Forrester Wave: Data Governance Solutions.

Data Quality 52

Data Quality Data Governance Cloud Data Data Engineering 52

Alation

JULY 7, 2022

To answer these questions we need to look at how data roles within the job market have evolved, and how academic programs have changed to meet new workforce demands. In the 2010s, the growing scope of the data landscape gave rise to a new profession: the data scientist. The data scientist.

Data Scientist 52

Data Scientist Data Analyst Analytics Analytics 52

phData

FEBRUARY 14, 2023

Insurance companies often face challenges with data silos and inconsistencies among their legacy systems. To address these issues, they need a centralized and integrated data platform that serves as a single source of truth, preferably with strong data governance capabilities.

Data Governance 52

Data Governance Data Silos Predictive Analytics Data Scientist 52

phData

MARCH 22, 2023

Snowpark , an innovative technology from the Snowflake Data Cloud , promises to meet this demand by allowing data scientists to develop complex data transformation logic using familiar programming languages such as Java, Scala, and Python.

ML 52

ML ML Python Machine Learning 52

Pickl AI

OCTOBER 11, 2023

Key Features Benefit from the real-time surveillance thus, it helps in identifying potential issues in real-time It comes with advanced analytical capacities contributing to well-informed decision-making; Intuitively explore and grasp the intricacies of data. In such a case, you need to integrate with the Data Observability platform.

Data Observability 52

Data Observability Data Quality Data Pipeline Data Governance 52

Alation

DECEMBER 13, 2022

People come to the data catalog to find trusted data, understand it, and use it wisely. Today a modern catalog hosts a wide range of users (like business leaders, data scientists and engineers) and supports an even wider set of use cases (like data governance , self-service , and cloud migration ).

DataOps 52

DataOps Data Governance Data Silos Data Scientist 52

DagsHub

AUGUST 9, 2023

Data Scientists don’t usually spend time understanding legalese just as most non-data scientists have no idea what is involved in fine-tuning a Large-Language-Model (LLM). So, when a new rule comes out we prefer to let the legal team handle it and assume they will let us know if anything is relevant.

AI 52

AI AI Data Governance Data Scientist 52