ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Dataconomy

FEBRUARY 12, 2024

This shift leverages the capabilities of modern data warehouses, enabling faster data ingestion and reducing the complexities associated with traditional transformation-heavy ETL processes. These platforms provide a unified view of data, enabling businesses to derive insights from diverse datasets efficiently. Image credit ) 5.

ETL 195

ETL Data Governance Machine Learning Machine Learning 195

The MLOps Blog

JUNE 27, 2023

With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

IBM Journey to AI blog

JANUARY 17, 2023

IBM Cloud Pak for Data Express solutions provide new clients with affordable and high impact capabilities to expeditiously explore and validate the path to become a data-driven enterprise. IBM Cloud Pak for Data Express solutions offer clients a simple on ramp to start realizing the business value of a modern architecture.

Data Governance 58

Data Governance Data Science AI AI 58

Pickl AI

NOVEMBER 4, 2024

Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? They are crucial in ensuring data is readily available for analysis and reporting. from 2025 to 2030.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

ODSC - Open Data Science

JUNE 19, 2023

In particular, its progress depends on the availability of related technologies that make the handling of huge volumes of data possible. These technologies include the following: Data governance and management  — It is crucial to have a solid data management system and governance practices to ensure data accuracy, consistency, and security.

Big Data 96

Big Data Big Data Artificial Intelligence Artificial Intelligence 96

Smart Data Collective

OCTOBER 17, 2022

A potential option is to use an ELT system — extract, load, and transform — to interact with the data on an as-needed basis. It may conflict with your data governance policy (more on that below), but it may be valuable in establishing a broader view of the data and directing you toward better data sets for your main models.

Big Data 119

Big Data Big Data Data Engineering Data Engineering 119

Pickl AI

OCTOBER 11, 2023

What is Data Observability? It is the practice of monitoring, tracking, and ensuring data quality, reliability, and performance as it moves through an organization’s data pipelines and systems. Data quality tools help maintain high data quality standards. Tools Used in Data Observability?

Data Observability 52

Data Observability Data Quality Data Pipeline Data Governance 52

Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineer Data Engineering 91

Pickl AI

OCTOBER 10, 2023

In today’s fast-paced business environment, the significance of Data Observability cannot be overstated. Data Observability enables organizations to detect anomalies, troubleshoot issues, and maintain data pipelines effectively. How Are Data Quality and Data Observability Similar—and How Are They Different?

Data Observability 52

Data Observability Data Quality Data Governance Data Pipeline 52

IBM Journey to AI blog

AUGUST 12, 2024

It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

Big Data 106

Big Data Big Data ML ML 106

Alation

DECEMBER 23, 2021

And even though Santa had made the leap to the data cloud — using Snowflake of course — making a list of all the data turned out to be tougher than finding a polar bear in a snowstorm. And Santa was hoping to make 2021 his most data-driven year yet. For the first time, data governance was no longer a naughty concept.

Data Governance 59

Data Governance Data Pipeline Tableau Big Data 59

ODSC - Open Data Science

FEBRUARY 5, 2025

These virtual, self-paced resources will help attendees brush up on essential data science and AI skills before diving into the main event. Attendees can immerse themselves in hands-on training sessions, workshops, and deep dives into AI engineering, large language models, and data science best practices.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

APRIL 26, 2023

As you can imagine, data science is a pretty loose term or big tent idea overall. Though just about every industry imaginable utilizes the skills of a data-focused professional, each has its own challenges, needs, and desired outcomes. What makes this job title unique is the “Swiss army knife” approach to data.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

DataRobot Blog

OCTOBER 3, 2017

Additionally, Alation and Paxata announced the new data exploration capabilities of Paxata in the Alation Data Catalog, where users can find trusted data assets and, with a single click, work with their data in Paxata’s Self-Service Data Prep Application. 3) Data professionals come in all shapes and forms.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 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

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 133

AWS ML ML Machine Learning 133

phData

NOVEMBER 9, 2023

The main goal of a data mesh structure is to drive: Domain-driven ownership Data as a product Self-service infrastructure Federated governance One of the primary challenges that organizations face is data governance.

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

IBM Journey to AI blog

JULY 13, 2023

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Data science tasks such as machine learning also greatly benefit from good data integrity.

Data Quality 74

Data Quality Data Profiling Data Governance Analytics 74

phData

MARCH 22, 2023

Top Use Cases of Snowpark With Snowpark, bringing business logic to data in the cloud couldn’t be easier. Transitioning work to Snowpark allows for faster ML deployment, easier scaling, and robust data pipeline development. ML Applications For data scientists, models can be developed in Python with common machine learning tools.

ML 52

ML ML Python Machine Learning 52

Pickl AI

OCTOBER 17, 2024

Automation Automation plays a pivotal role in streamlining ETL processes, reducing the need for manual intervention, and ensuring consistent data availability. By automating key tasks, organisations can enhance efficiency and accuracy, ultimately improving the quality of their data pipelines.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

phData

MAY 28, 2024

The combination of these capabilities allows organizations to quickly implement advanced analytics without the need for extensive data science expertise. With the ability to write custom functions and procedures, developers can create sophisticated data pipelines and analytical workflows, all within the same unified environment.

Machine Learning 52

Machine Learning Machine Learning Data Engineering Data Engineering 52

DagsHub

AUGUST 23, 2024

We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. Data Governance Data governance is the foundation of any data quality framework. It primarily caters to large organizations with complex data environments.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. What does a modern data architecture do for your business?

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

Alation

JANUARY 17, 2023

Powered by cloud computing, more data professionals have access to the data, too. Data analysts have access to the data warehouse using BI tools like Tableau; data scientists have access to data science tools, such as Dataiku. Better Data Culture. Who Can Adopt the Modern Data Stack?

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

ODSC - Open Data Science

DECEMBER 3, 2024

While the concept of data mesh as a data architecture model has been around for a while, it was hard to define how to implement it easily and at scale. Two data catalogs went open-source this year, changing how companies manage their data pipeline. The departments closest to data should own it.

Data Pipeline 40

Data Pipeline Data Governance Data Analyst Analytics 40

Pickl AI

OCTOBER 10, 2024

Support for Advanced Analytics : Transformed data is ready for use in Advanced Analytics, Machine Learning, and Business Intelligence applications, driving better decision-making. Compliance and Governance : Many tools have built-in features that ensure data adheres to regulatory requirements, maintaining data governance across organisations.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Pickl AI

OCTOBER 15, 2024

Apache Nifi Apache Nifi is an open-source ETL tool that automates data flow between systems. It is well known for its data provenance and seamless data routing capabilities. Nifi provides a graphical interface for designing data pipelines , allowing users to track data flows in real-time.

ETL 52

ETL Azure AWS Data Governance 52

Alation

JULY 7, 2022

Universities were only just beginning to plan formal academic data science programs, and the skills to be taught in those programs were still being identified. This year, there are more than 900 academic programs offering training in data science. A lack of data literacy slows down the process.

Data Scientist 52

Data Scientist Data Analyst Analytics Analytics 52

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

DECEMBER 11, 2024

Focusing only on what truly matters reduces data clutter, enhances decision-making, and improves the speed at which actionable insights are generated. Streamlined Data Pipelines Efficient data pipelines form the backbone of lean data management.

Data Silos 52

Data Silos Data Pipeline Artificial Intelligence Artificial Intelligence 52

Pickl AI

JULY 24, 2024

Strategies to Improve Data Quality High-quality data is a strategic asset that fuels innovation, drives informed decision-making, and enhances operational efficiency. Data Governance and Management Effective data governance is the cornerstone of data quality.

Data Quality 52

Data Quality Machine Learning Machine Learning Clean Data 52

Pickl AI

OCTOBER 17, 2024

Apache NiFi As an open-source data integration tool, Apache NiFi enables seamless data flow and transformation across systems. Its drag-and-drop interface simplifies the design of data pipelines, making it easier for users to implement complex transformation logic.

ETL 52

ETL Data Warehouse SQL Data Quality 52

The MLOps Blog

MARCH 20, 2023

Learn more Version Control for Machine Learning and Data Science Dataset version management challenges Data storage and retrieval As a machine learning project advances in its lifecycle, its demand for data also increases. Data aggregation: Data sources could increase as more data points are required to train ML models.

ML 59

ML ML Machine Learning Machine Learning 59

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

Analytics 69

Analytics Analytics Data Silos Data Lakes 69