AWS Big Data

MARCH 13, 2024

You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches. Athena is used to run geospatial queries on the location data stored in the S3 buckets. Choose Run. You’re now ready to query the tables using Athena.

Analytics 121

Analytics IoT Metadata Internet of Things 121

AWS Big Data

MARCH 9, 2023

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Slice and Dice 98

Slice and Dice Dashboards Analytics Interactive 98

AWS Big Data

JULY 20, 2023

The system ingests data from various sources such as cloud resources, cloud activity logs, and API access logs, and processes billions of messages, resulting in terabytes of data daily. This data is sent to Apache Kafka, which is hosted on Amazon Managed Streaming for Apache Kafka (Amazon MSK).

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

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. Let’s take an example. The marketing team created leads based on the event in Adobe Marketo.

Sales 95

Sales Visualization Software Marketing 95

AWS Big Data

MARCH 3, 2023

The Delta tables created by the EMR Serverless application are exposed through the AWS Glue Data Catalog and can be queried through Amazon Athena. Data ingestion – Steps 1 and 2 use AWS DMS, which connects to the source database and moves full and incremental data (CDC) to Amazon S3 in Parquet format.

Data Lake 98

Data Lake Dashboards Metrics Metadata 98

IBM Big Data Hub

JULY 17, 2023

foundation models to help users discover, augment, and enrich data with natural language. Watsonx.data is built on 3 core integrated components: multiple query engines, a catalog that keeps track of metadata, and storage and relational data sources which the query engines directly access.

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

Alation

JANUARY 17, 2023

These help data analysts visualize key insights that can help you make better data-backed decisions. ELT Data Transformation Tools: ELT data transformation tools are used to extract, load, and transform your data. Examples of data transformation tools include dbt and dataform.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

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 83

Data Lake Metadata Business Analysis Data-driven 83

AWS Big Data

DECEMBER 4, 2024

Institutional Data & AI Platform architecture The Institutional Division has implemented a self-service data platform to enable the domain teams to build and manage data products autonomously. The following diagram illustrates the building blocks of the Institutional Data & AI Platform.

Metadata 104

Metadata Data Governance Data Quality Data-driven 104

erwin

APRIL 16, 2024

In this blog, we’ll delve into the critical role of governance and data modeling tools in supporting a seamless data mesh implementation and explore how erwin tools can be used in that role. erwin also provides data governance, metadata management and data lineage software called erwin Data Intelligence by Quest.

Metadata 52

Metadata Data Quality Data Governance Modeling 52

AWS Big Data

OCTOBER 30, 2024

Publish data assets – As the data producer from the retail team, you must ingest individual data assets into Amazon DataZone. For this use case, create a data source and import the technical metadata of four data assets— customers , order_items , orders , products , reviews , and shipments —from AWS Glue Data Catalog.

Visualization 119

Visualization Data Lake Testing Data Governance 119

Cloudera

NOVEMBER 22, 2021

In addition, more data is becoming available for processing / enrichment of existing and new use cases e.g., recently we have experienced a rapid growth in data collection at the edge and an increase in availability of frameworks for processing that data. As a result, alternative data integration technologies (e.g.,

Data Processing 100

Data Processing Data Warehouse Enterprise Visualization 100

Octopai

NOVEMBER 13, 2022

The modern data stack is a data management system built out of cloud-based data systems. A given modern data stack will usually include components for data ingestion from your data sources, data transformation, data storage, data analysis and reporting.

Enterprise 97

Enterprise Data Warehouse Reporting Metadata 97

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 115

Data Lake Data Warehouse B2B Data-driven 115

Alation

JULY 18, 2022

To ensure you can deliver on this world-changing vision of data, Alation helps you maximize the value of your data lake with integrations to the Unity catalog. Alation will leverage the Databricks Unity Catalog so users can easily integrate metadata from multiple workspaces, powering discovery, governance, and insights inside Alation.

ROI 52

ROI Metadata Data Lake Digital Transformation 52

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

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