Tableau

MAY 11, 2022

Certified data sources carefully chosen by site administrators and project leaders. Recommended data sources personally certified and/or automatically selected based on organizational usage patterns. Recommended database tables that are used frequently in data sources and workbooks published to your Tableau server.

Tableau 92

Tableau Data Warehouse Database Data Analyst 92

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Business Intelligence 52

Pickl AI

APRIL 6, 2023

Understand the fundamentals of data engineering: To become an Azure Data Engineer, you must first understand the concepts and principles of data engineering. Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required. Data Warehousing concepts and knowledge should be strong.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data Science 84

Data Science Analytics Analytics Data Scientist 84

Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Snowflake Database Pros Extensive Storage Opportunities Snowflake provides affordability, scalability, and a user-friendly interface.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. ArangoDB ArangoDB is a company that provides a database platform for graph and document data.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. This type of next-generation data store combines a data lake’s flexibility with a data warehouse’s performance and lets you scale AI workloads no matter where they reside.

AI 78

AI AI Data Warehouse ML 78

phData

SEPTEMBER 28, 2023

Dataflows represent a cloud-based technology designed for data preparation and transformation purposes. Dataflows have different connectors to retrieve data, including databases, Excel files, APIs, and other similar sources, along with data manipulations that are performed using Online Power Query Editor.

Power BI 78

Power BI Data Preparation Machine Learning Machine Learning 78

Alation

FEBRUARY 13, 2020

Just as you need data about finances for effective financial management, you need data about data (metadata) for effective data management. You can’t manage data without metadata. But data catalogs do much more. Figure 1 shows a logical data model that represents typical metadata content of a data catalog.

Data Lakes 52

Data Lakes Data Governance Data Science Data Analyst 52

DagsHub

DECEMBER 11, 2023

DagsHub DagsHub is a centralized Github-based platform that allows Machine Learning and Data Science teams to build, manage and collaborate on their projects. In addition to versioning code, teams can also version data, models, experiments and more. However, these tools have functional gaps for more advanced data workflows.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

ODSC - Open Data Science

MARCH 23, 2023

Real-time Analytics & Built-in Machine Learning Models with a Single Database Akmal Chaudhri, Senior Technical Evangelist at SingleStore, explores the importance of delivering real-time experiences in today’s big data industry and how data models and algorithms rely on powerful and versatile data infrastructure.

Data Science 52

Data Science Data Lakes Machine Learning Machine Learning 52

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. Relational database connectors are available. Talend Free to use.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS 82

AWS Data Lakes Clustering Data Preparation 82

Flipboard

OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Text, images, audio, and videos are common examples of unstructured data. Understanding the data, categorizing it, storing it, and extracting insights from it can be challenging.

AWS 132

AWS ML ML Analytics 132

Pickl AI

JUNE 28, 2023

Schema Integration Schema integration deals with reconciling data stored in different database schemas or structures. It involves mapping and transforming data elements to align with a unified schema. It ensures that the integrated data is available for analysis and reporting. Wrapping It Up !!!

Data Mining 52

Data Mining Data Mining Data Mining ETL 52

Tableau

MARCH 10, 2022

Salesforce CDP creates holistic customer views by pulling data from internal and external databases and building unified customer profiles. Within TCRM’s dashboard designer, you can use three object types to create visualizations: Data lake objects provide access to data ingested from various connected data sources.

Tableau 95

Tableau Data Lakes Analytics Analytics 95

Tableau

MARCH 10, 2022

Salesforce CDP creates holistic customer views by pulling data from internal and external databases and building unified customer profiles. Within TCRM’s dashboard designer, you can use three object types to create visualizations: Data lake objects provide access to data ingested from various connected data sources.

Tableau 88

Tableau Data Lakes Analytics Analytics 88

The MLOps Blog

JUNE 27, 2023

Model versioning, lineage, and packaging : Can you version and reproduce models and experiments? Can you see the complete model lineage with data/models/experiments used downstream? Dolt Dolt is an open-source relational database system built on Git. Is it fast and reliable enough for your workflow?

Machine Learning 134

Machine Learning Machine Learning ML ML 134

O'Reilly Media

OCTOBER 19, 2021

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses.

ML 138

ML ML Data Scientist AWS 138