Jet Global

FEBRUARY 23, 2024

This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making. 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 Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

AWS Big Data

APRIL 25, 2023

On many occasions, they need to apply business logic to the data received from the source SaaS platform before pushing it to the target SaaS platform. AnyCompany’s marketing team hosted an event at the Anaheim Convention Center, CA. The marketing team created leads based on the event in Adobe Marketo. Let’s take an example.

Sales 74

Sales Visualization Software Marketing 74

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. How does Data Virtualization complement Data Warehousing and SOA Architectures?

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Curated foundation models, such as those created by IBM or Microsoft, help enterprises scale and accelerate the use and impact of the most advanced AI capabilities using trusted data.

Risk 73

Risk Modeling Management Metadata 73

Cloudera

DECEMBER 9, 2022

Developers need to onboard new data sources, chain multiple data transformation steps together, and explore data as it travels through the flow. This allows developers to make changes to their processing logic on the fly while running some test data through their flow and validating that their changes work as intended.

Testing 98

Testing Cost-Benefit Interactive Visualization 98

datapine

SEPTEMBER 29, 2022

This person (or group of individuals) ensures that the theory behind data quality is communicated to the development team. 2 – Data profiling. Data profiling is an essential process in the DQM lifecycle. This means there are no unintended data errors, and it corresponds to its appropriate designation (e.g.,

Data Quality 241

Data Quality Metrics Data-driven Management 241

AWS Big Data

JANUARY 27, 2023

With a unified data catalog, you can quickly search datasets and figure out data schema, data format, and location. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos. Refer to Catalogs for more information.

Data Lake 63

Data Lake Metadata Business Analysis Data-driven 63

erwin

SEPTEMBER 17, 2020

Business terms and data policies should be implemented through standardized and documented business rules. Compliance with these business rules can be tracked through data lineage, incorporating auditability and validation controls across data transformations and pipelines to generate alerts when there are non-compliant data instances.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

AWS Big Data

OCTOBER 11, 2023

Due to this low complexity, the solution uses AWS serverless services to ingest the data, transform it, and make it available for analytics. The architecture uses AWS Lambda , a serverless, event-driven compute service that lets you run code without provisioning or managing servers.

Visualization 99

Visualization Dashboards Data-driven Gap analysis 99

AWS Big Data

MARCH 15, 2023

Performance and scalability of both the data pipeline and API endpoint were key success criteria. The data pipeline needed to have sufficient performance to allow for fast turnaround in the event that data issues needed to be corrected.

Cost-Benefit 68

Cost-Benefit Data Processing Optimization Data-driven 68

AWS Big Data

AUGUST 1, 2023

For GlueDatabaseName , enter a unique name for the Data Catalog database to hold the Jira data table metadata (the default is jiralake ). This mode will scan all data and disable the change data capture (CDC) features of the stack. The DataBrew job performs data transformation and filtering tasks.

Data Lake 88

Data Lake Data Transformation Cost-Benefit Data-driven 88

Jet Global

MARCH 14, 2024

Data Lineage and Documentation Jet Analytics simplifies the process of documenting data assets and tracking data lineage in Fabric. It offers a transparent and accurate view of how data flows through the system, ensuring robust compliance. I understand that I can withdraw my consent at any time. Privacy Policy.

Analytics 52

Analytics Management Reporting Enterprise 52

AWS Big Data

DECEMBER 21, 2023

To ingest the data, smava uses a set of popular third-party customer data platforms complemented by custom scripts. After the data lands in Amazon S3, smava uses the AWS Glue Data Catalog and crawlers to automatically catalog the available data, capture the metadata, and provide an interface that allows querying all data assets.

Data Lake 91

Data Lake Data Warehouse Data-driven B2B 91

Jet Global

JULY 19, 2023

Data Connectivity Enhancements Data and content authors are the first users in the app building infrastructure and content. It is important for our customers to access advanced connectors and data transformation features so they can build a robust data layer. I understand that I can withdraw my consent at any time.

Dashboards 52

Dashboards Visualization Reporting Interactive 52

AWS Big Data

JANUARY 17, 2024

Incremental query refers to a query strategy that focuses on processing and analyzing only the new or updated data within a data lake since the last query. The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query.

Data Lake 96

Data Lake Snapshot Big Data Data-driven 96

Jet Global

MAY 1, 2023

Requirement Multi-Source Data Blending Data from multiple sources is compiled and the output is a single view, metric, or visualization. Data Transformation and Enrichment Data can be enriched for analysis. Metadata Self-service analysis is made easy with user-friendly naming conventions for tables and columns.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52