IBM Journey to AI blog

JULY 24, 2024

By providing access to a wider pool of trusted data, it enhances the relevance and precision of AI models, accelerating innovation in these areas. Optimizing performance with fit-for-purpose query engines In the realm of data management, the diverse nature of data workloads demands a flexible approach to query processing.

Data Governance 69

Data Governance Data Warehouse Data Preparation AI 69

IBM Journey to AI blog

OCTOBER 12, 2023

Connecting AI models to a myriad of data sources across cloud and on-premises environments AI models rely on vast amounts of data for training. Once trained and deployed, models also need reliable access to historical and real-time data to generate content, make recommendations, detect errors, send proactive alerts, etc.

AI 93

AI AI Data Scientist Data Governance 93

DataRobot

JANUARY 20, 2022

— Collaborating via Snowflake Data Cloud and DataRobot AI Cloud Platform will enable multiple organizations to build a community movement where experimentation, innovation, and creativity flourish. Specialized teams from DataRobot and Snowflake will enable ICSs to mitigate data governance and model bias risk with confidence.

Data Warehouse 145

Data Warehouse Data Science Analytics Analytics 145

Dataconomy

SEPTEMBER 4, 2023

Best practices in cloud analytics are essential to maintain data quality, security, and compliance ( Image credit ) Data governance: Establish robust data governance practices to ensure data quality, security, and compliance.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Dataconomy

FEBRUARY 23, 2023

They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

Big Data 91

Big Data Big Data Data Engineer Data Engineering 91

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. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

AI 98

AI AI Tableau Data Scientist 98

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. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

AI 95

AI AI Tableau Data Scientist 95

Flipboard

MAY 30, 2025

For the preceding techniques, the foundation should provide scalable infrastructure for data storage and training, a mechanism to orchestrate tuning and training pipelines, a model registry to centrally register and govern the model, and infrastructure to host the model. Model versions should be managed centrally in a model registry.

AWS 141

AWS AI AI Database 141

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Smart Data Collective

OCTOBER 17, 2022

Cloud data warehouses provide various advantages, including the ability to be more scalable and elastic than conventional warehouses. Can’t get to the data. All of this data might be overwhelming for engineers who struggle to pull in data sets quickly enough. Data pipeline maintenance.

Big Data 119

Big Data Big Data Data Engineer Data Engineering 119

Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

Data Engineer 52

Data Engineer Data Engineering 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. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

SEPTEMBER 19, 2023

Cost reduction by minimizing data redundancy, improving data storage efficiency, and reducing the risk of errors and data-related issues. Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

IBM Journey to AI blog

AUGUST 4, 2023

When done well, data democratization empowers employees with tools that let everyone work with data, not just the data scientists. When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?

Data Lakes 73

Data Lakes AI AI Data Governance 73

Dataconomy

APRIL 6, 2023

Data democratization is a term that has been gaining traction in recent years, referring to the process of making data more accessible and usable for a wider range of people. Essentially, it involves removing barriers to accessing and using data so that it is no longer the exclusive domain of data scientists and other experts.

Data Governance 70

Data Governance Data Silos Data Analysis Data Analysis 70

Alation

JUNE 9, 2021

In this four-part blog series on data culture, we’re exploring what a data culture is and the benefits of building one, and then drilling down to explore each of the three pillars of data culture – data search & discovery, data literacy, and data governance – in more depth.

Data Governance 59

Data Governance Database Cloud Data Machine Learning 59

Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

Data Governance 59

Data Governance ML ML Cloud Data 59

IBM Journey to AI blog

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

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. Understand.

Data Governance 52

Data Governance Data Scientist Data Engineer Data Engineering 52

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. The good news is that there are many skills that data scientists already have that are transferable to data engineering.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Alation

DECEMBER 15, 2022

August 2020: Constellation Research names Alation to its Constellation Shortlist for Metadata Management, Data Cataloging & Data Governance in Q3 2020 and Alation customer Cisco wins the Constellation SuperNova Award for Use of Alation for Data Governance. Is Alation the GOAT of Data Catalogs?

Data Governance 52

Data Governance Data Quality Data Warehouse Data Scientist 52

Pickl AI

AUGUST 9, 2024

Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Students should learn about the architecture of data warehouses and how they differ from traditional databases.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. Jason: I’m curious to learn about your modern data stack. Happy to chat.

Data Analyst 52

Data Analyst Data Scientist Analytics Analytics 52

IBM Journey to AI blog

MARCH 14, 2024

. With Db2 Warehouse’s fully managed cloud deployment on AWS, enjoy no overhead, indexing, or tuning and automated maintenance.  Netezza incorporates in-database analytics and machine learning (ML), governance, security and patented massively parallel processing. . Netezza

AWS 93

AWS Database ETL AI 93

phData

APRIL 18, 2023

Snowflake enables organizations to instantaneously scale to meet SLAs with timely delivery of regulatory obligations like SEC Filings, MiFID II, Dodd-Frank, FRTB, or Basel III—all with a single copy of data enabled by data sharing capabilities across various internal departments.

Data Silos 52

Data Silos ETL Clustering Analytics 52

DagsHub

AUGUST 23, 2024

Data quality is crucial across various domains within an organization. For example, software engineers focus on operational accuracy and efficiency, while data scientists require clean data for training machine learning models. Without high-quality data, even the most advanced models can't deliver value.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

APRIL 26, 2024

Data Warehousing and ETL Processes What is a data warehouse, and why is it important? A data warehouse is a centralised repository that consolidates data from various sources for reporting and analysis. It is essential to provide a unified data view and enable business intelligence and analytics.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

JULY 29, 2024

Data Governance and Security Hadoop clusters often handle sensitive data, making data governance and security a significant concern. Ensuring compliance with regulations such as GDPR or HIPAA requires implementing robust security measures, including data encryption, access controls, and auditing capabilities.

Hadoop 52

Hadoop Clustering Big Data Big Data 52

The MLOps Blog

MARCH 15, 2023

Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. Data governance: Ensure that the data used to train and test the model, as well as any new data used for prediction, is properly governed.

AWS 52

AWS ETL ML ML 52

Alation

OCTOBER 21, 2021

But, on the back end, data lakes give businesses a common repository to collect and store data, streamlined usage from a single source, and access to the raw data necessary for today’s advanced analytics and artificial intelligence (AI) needs. Irrelevant data. Ungoverned data. Subscribe to Alation's Blog.

Data Lakes 52

Data Lakes Data Governance Data Warehouse Business Intelligence 52

Alation

APRIL 14, 2023

People were familiar with the value of a data catalog (and the growing need for data governance ), though many admitted to being somewhat behind on their journeys. Potent presentations DJ Patil served as the first Chief Data Scientist of the United States under Obama, and he kicked off the conference with a riveting keynote.

Data Pipeline 52

Data Pipeline Data Warehouse Cloud Data ETL 52

phData

SEPTEMBER 27, 2024

Here’s how a composable CDP might incorporate the modeling approaches we’ve discussed: Data Storage and Processing : This is your foundation. You might choose a cloud data warehouse like the Snowflake AI Data Cloud or BigQuery. Building a composable CDP requires some serious data engineering chops.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

Pickl AI

JANUARY 19, 2025

Data warehousing refers to the process of collecting, storing, and managing large volumes of structured data from various sources in a central repository known as a data warehouse. Integrated : Data from multiple sources is consolidated into a single coherent framework.

Data Mining 52

Data Mining Data Mining Data Mining Data Warehouse 52

Precisely

APRIL 15, 2025

Strengthen governance frameworks to ensure AI models operate with full transparency and accountability. Tooling gaps: relying on legacy solutions instead of AI-focused observability tools Most organizations still depend on traditional BI, data warehouses, and data integration tools for observability.

Data Observability 69

Data Observability Data Quality Data Pipeline ML 69