AWS Big Data

JULY 25, 2024

Data Pipeline has been a foundational service for getting customer off the ground for their extract, transform, load (ETL) and infra provisioning use cases. Before starting any production workloads after migration, you need to test your new workflows to ensure no disruption to production systems. Choose ETL jobs.

Visualization 116

Visualization Management Data Integration Testing 116

AWS Big Data

OCTOBER 4, 2023

Due to this limitation, the cost of failures of long-running extract, transform, and load (ETL) and batch queries on Trino was high in terms of completion time, compute wastage, and spend. These new enhancements in Trino with Amazon EMR provide improved resiliency for running ETL and batch workloads on Spot Instances with reduced costs.

Big Data 115

Big Data Optimization Data-driven Management 115

AWS Big Data

JULY 20, 2023

We illustrate a cross-account sharing use case, where a Lake Formation steward in producer account A shares a federated Hive database and tables using LF-Tags to consumer account B. The admin continues to set up Lake Formation tag-based access control (LF-TBAC) on the federated Hive database and share it to account B.

Data Lake 98

Data Lake Metadata Data Processing Big Data 98

Cloudera

SEPTEMBER 28, 2020

They have dev, test, and production clusters running critical workloads and want to upgrade their clusters to CDP Private Cloud Base. Hive-on-Tez for better ETL performance. Customer Environment: The customer has three environments: development, test, and production. Test and QA. Test and QA. Background: .

Testing 132

Testing Metadata Risk Data Science 132

AWS Big Data

NOVEMBER 17, 2023

It also applies general software engineering principles like integrating with git repositories, setting up DRYer code, adding functional test cases, and including external libraries. Tests – These are assertions you make about your models and other resources in your dbt project (such as sources, seeds, and snapshots). project-dir.

Snapshot 111

Snapshot Data Processing Testing Data Warehouse 111

AWS Big Data

JUNE 10, 2024

Test access to the producer cataloged Amazon S3 data using EMR Serverless in the consumer account. Test access using Athena queries in the consumer account. Test access using SageMaker Studio in the consumer account. It is recommended to use test accounts. The producer account will host the EMR cluster and S3 buckets.

Data Lake 118

Data Lake Metadata Data Warehouse Data Processing 118

Cloudera

JUNE 26, 2023

Spark is primarily used to create ETL workloads by data engineers and data scientists. Impala only masquerades as an ETL pipeline tool: use NiFi or Airflow instead It is common for Cloudera Data Platform (CDP) users to ‘test’ pipeline development and creation with Impala because it facilitates fast, iterate development and testing.

Data Processing 76

Data Processing Testing Visualization Data Science 76

AWS Big Data

JULY 26, 2023

The following are common asks from our customers: Is it possible to develop and test AWS Glue data integration jobs on my local laptop? The software development lifecycle on AWS defines the following six phases: Plan, Design, Implement, Test, Deploy, and Maintain. Test In the testing phase, you check the implementation for bugs.

Data Integration 98

Data Integration Snapshot Testing Visualization 98

erwin

NOVEMBER 6, 2020

Import existing Excel or CSV files, use the drag-and-drop feature to extract the mappings from your ETL scripts, or manually populate the inventory to then be visualized with the lineage analyzer. Data Cataloging: Catalog and sync metadata with data management and governance artifacts according to business requirements in real time.

Data Governance 97

Data Governance Metadata Data-driven Visualization 97

erwin

FEBRUARY 19, 2021

It’s easier to map, move and test data for regular maintenance of existing structures, movement from legacy systems to new systems during a merger or acquisition or a modernization effort. Quicker project delivery. Greater productivity & reduced costs. Digital transformation.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

AWS Big Data

JULY 18, 2024

A data domain producer maintains its own ETL stack using AWS Glue , AWS Lambda to process, AWS Glue Databrew to profile the data and prepare the data asset (data product) before cataloguing it into AWS Glue Data Catalog in their account. Producers ingest data into their S3 buckets through pipelines they manage, own, and operate.

Data Lake 130

Data Lake Publishing Metadata Data-driven 130

AWS Big Data

NOVEMBER 15, 2023

Additionally, it manages table definitions in the AWS Glue Data Catalog , containing references to data sources and targets of extract, transform, and load (ETL) jobs in AWS Glue. It is possible to define stages (DEV, INT, PROD) in each layer to allow structured release and test without affecting PROD.

Analytics 111

Analytics Dashboards Metadata Data Warehouse 111

AWS Big Data

APRIL 3, 2024

Synthea is a synthetic patient generator that creates realistic patient data and associated medical records that can be used for testing healthcare software applications. To learn more about Pydeequ as a data testing framework, see Testing Data quality at scale with Pydeequ. onData(df).useRepository(metricsRepository).addCheck(

Data Quality 119

Data Quality Visualization Metadata Metrics 119

AWS Big Data

JULY 25, 2024

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it straightforward and cost-effective to analyze all your data using standard SQL and your existing extract, transform, and load (ETL); business intelligence (BI); and reporting tools. Add any necessary tags to the instance.

Management 116

Management Data Warehouse Big Data Testing 116

AWS Big Data

FEBRUARY 9, 2023

Developers need to understand the application APIs, write implementation and test code, and maintain the code for future API changes. Optionally, provide a description for the flow and tags. Test the solution Log in to your Salesforce account, and edit any record in the Account object. Choose Create flow. Choose Next.

Data Warehouse 105

Data Warehouse Data-driven Snapshot Testing 105

erwin

JANUARY 11, 2019

It’s obvious that the manual road is very challenging to discover and synthesize data that resides in different formats in thousands of unharvested, undocumented databases, applications, ETL processes and procedural code.

Data Governance 40

Data Governance Risk Metadata Management 40

AWS Big Data

JUNE 6, 2023

In order to mature our data marts, it became clear that we needed to provide Analysts and other data consumers with all tracked digital analytics data in our DWH as they depend on it for analyses, reporting, campaign evaluation, product development and A/B testing. We made sure to add appropriate tags for cost monitoring.

Analytics 59

Analytics Data Lake Testing Optimization 59

Alation

OCTOBER 27, 2022

This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. First, we pulled unique contact titles into a Google Sheet from Hubspot and other sources, and tagged them with internal attributes; this helps downstream analysts get work done.

Dashboards 52

Dashboards Metrics Sales Reporting 52

Alation

JANUARY 25, 2022

But social extends even further; it includes the comments users make, assets they flag as favorites, the watches they set on assets, who they tag and how, and their level of expertise. People often use the same data sources, so capturing knowledge about that usage, (including their role, team, and expertise), layers on important context.

Metadata 52

Metadata IT Data-driven Metrics 52

AWS Big Data

APRIL 21, 2025

The data engineering team owns the extract, transform, and load (ETL) application that will process the raw data to create and maintain the Iceberg tables. The ETL application will use IAM role-based access to the Iceberg table, and the data analyst gets Lake Formation permissions to query the same tables. Choose Grant.

Data Lake 93

Data Lake Metadata Interactive Big Data 93

AWS Big Data

MAY 9, 2025

Built-in zero-ETL connectors reduce data silos by integrating various data sources, enabling unified analytics across teams. Under LF-Tags or catalog resources , select Named Data Catalog resources. The Lake Formation admin verifies that the shared resources are accessible by running test queries in Athena. Choose Grant.

Data Lake 66

Data Lake Data Warehouse Marketing Management 66

AWS Big Data

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

Data Warehouse 113

Data Warehouse Metadata Publishing Sales 113