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. But what does this mean from a practitioner perspective?

Metrics 52

Metrics Dashboards Sales Reporting 52

Alation

JANUARY 17, 2023

Great data science tools will assist data scientists and citizen data scientists in testing and training datasets for developing models, and ultimately for deploying them. An example of a data science tool is Dataiku. Business Intelligence Tools: Business intelligence (BI) tools are used to visualize your data.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

O'Reilly on Data

JANUARY 17, 2023

Upload your data, click through a workflow, walk away. If you’re a professional data scientist, you already have the knowledge and skills to test these models. That takes us to a conspicuous omission from that list of roles: the data scientists who focused on building basic models. Get your results in a few hours.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

AWS Big Data

JULY 6, 2023

It has not been specifically designed for heavy data transformation tasks. Configure the Step Functions workflow After you create the two Lambda functions, you can design the Step Functions workflow in the visual editor by using the Lambda Invoke and Map blocks, as shown in the following diagram. Add a data source block.

Machine Learning 130

Machine Learning Visualization IoT Digital Transformation 130

AWS Big Data

OCTOBER 31, 2023

If you’re testing on a different Amazon MWAA version, update the requirements file accordingly. For testing purposes, you can choose Add permissions and add the managed AmazonS3FullAccess policy to the user instead of providing restricted access. Modify the policy document with the DESC STORAGE INTEGRATION output values you recorded.

Data Processing 92

Data Processing Management Publishing Testing 92

IBM Big Data Hub

JULY 17, 2023

By supporting open-source frameworks and tools for code-based, automated and visual data science capabilities — all in a secure, trusted studio environment — we’re already seeing excitement from companies ready to use both foundation models and machine learning to accomplish key tasks.

Machine Learning 69

Machine Learning Data Warehouse Modeling Cost-Benefit 69

AWS Big Data

MARCH 3, 2023

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. Speed changes everything, and continuous testing across the entire CI/CD lifecycle is the key. Tricentis instills that confidence by providing software tools that enable Agile Continuous Testing (ACT) at scale.

Software 52

Software Data Lake Testing Cost-Benefit 52

Domino Data Lab

AUGUST 20, 2019

The problem is that a new unique identifier of a test example won’t be anywhere in the tree. Feature extraction means moving from low-level features that are unsuitable for learning—practically speaking, we get poor testing results—to higher-level features which are useful for learning. Separate out a hold-out test set.

Testing 68

Testing Modeling Interactive Measurement 68

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

FEBRUARY 23, 2024

Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation. Data mapping is important for several reasons.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MAY 1, 2023

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot. Their dashboards were visually stunning.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52