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. Figure 5: Parameter references in the configuration panel and auto-complete. Enabling self-service for developers. Interactivity when needed while saving costs.

Testing 104

Testing Cost-Benefit Interactive Visualization 104

AWS Big Data

APRIL 25, 2023

The following AWS services are used for data ingestion, processing, and load: Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications like Salesforce, SAP, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift , in just a few clicks.

Sales 80

Sales Visualization Software Metadata 80

AWS Big Data

JANUARY 17, 2024

Another popular transaction data lake use case is incremental query. 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. Melody Yang is a Senior Big Data Solution Architect for Amazon EMR at AWS.

Data Lake 106

Data Lake Snapshot Big Data Data-driven 106

AWS Big Data

OCTOBER 11, 2023

Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources. Data transformations through stored procedures and use of materialized views to curate datasets and generate insights is a known pattern with relational databases.

Management 118

Management Metadata Analytics Dashboards 118

AWS Big Data

APRIL 25, 2024

Alternatively, you can use AWS Glue for Apache Spark, which provides built-in support for bucketing configurations during the data transformation process. AWS Glue allows you to define bucketing parameters, such as the number of buckets and the columns to bucket on, providing an optimized data layout for efficient querying with Athena.

Optimization 70

Optimization Data Lake Cost-Benefit Reporting 70

AWS Big Data

NOVEMBER 15, 2023

For more information on this foundation, refer to A Detailed Overview of the Cost Intelligence Dashboard. It seamlessly consolidates data from various data sources within AWS, including AWS Cost Explorer (and forecasting with Cost Explorer ), AWS Trusted Advisor , and AWS Compute Optimizer.

Dashboards 105

Dashboards Analytics Metadata Data Warehouse 105

AWS Big Data

OCTOBER 11, 2023

Under the Transparency in Coverage (TCR) rule , hospitals and payors to publish their pricing data in a machine-readable format. For more information, refer to Delivering Consumer-friendly Healthcare Transparency in Coverage On AWS. The Data Catalog now contains references to the machine-readable data.

Visualization 110

Visualization Dashboards Data-driven Gap analysis 110

AWS Big Data

NOVEMBER 16, 2023

Data Vault 2.0 allows for the following: Agile data warehouse development Parallel data ingestion A scalable approach to handle multiple data sources even on the same entity A high level of automation Historization Full lineage support However, Data Vault 2.0 JOB_NAME All The process name from the ETL framework.

Enterprise 97

Enterprise Data Warehouse Data Lake Optimization 97

AWS Big Data

FEBRUARY 6, 2023

In the next sections, we explore the following topics: The DAG file, in order to understand how to define and then pass the correlation ID in the AWS Glue and EMR tasks The code needed in the Python scripts to output information based on the correlation ID Refer to the GitHub repo for the detailed DAG definition and Spark scripts.

Management 93

Management Interactive Metadata Publishing 93

Alation

JANUARY 17, 2023

A modern data stack relies on cloud computing, whereas a legacy data stack stores data on servers instead of in the cloud. Modern data stacks provide access for more data professionals than a legacy data stack. Examples of data transformation tools include dbt and dataform.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

AWS Big Data

MARCH 3, 2023

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. Let’s refer to this S3 bucket as the raw layer. Data transformation – Steps 3 and 4 represent an EMR Serverless Spark application (Amazon EMR 6.9

Data Lake 91

Data Lake Dashboards Metrics Metadata 91

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

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 115

Data Lake Management Metrics Data Warehouse 115

AWS Big Data

MARCH 15, 2023

To populate the database, the Infomedia team developed a data pipeline using Amazon Simple Storage Service (Amazon S3) for data storage, AWS Glue for data transformations, and Apache Hudi for CDC and record-level updates.

Cost-Benefit 74

Cost-Benefit Data Processing Optimization Data-driven 74

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 68

Data Lake Metadata Business Analysis 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. For full instructions, refer to Jira Cloud connector for Amazon AppFlow.

Data Lake 93

Data Lake Data Transformation Cost-Benefit Data-driven 93

Alation

NOVEMBER 22, 2022

Before you implement a data governance framework, you need to know the data you already have. This means you need to: Inventory data: Know all information resources and relevant metadata. Classify data: Organize structured and unstructured data into relevant categories. Reuse metadata productively.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

DataKitchen

JANUARY 23, 2023

It is important to have additional tools and processes in place to understand the impact of data errors and to minimize their effect on the data pipeline and downstream systems. These operations can include data movement, validation, cleaning, transformation, aggregation, analysis, and more.

Testing 130

Testing Data Governance Data Quality Data-driven 130

CIO Business Intelligence

APRIL 30, 2024

Einstein Copilot for Tableau remains in beta, but Tableau announced two new features for the AI assistant as well: AI-assisted data transformation. This feature can automate a data transformation pipeline with step-by-step suggestions for preparing data for analysis.

Analytics 96

Analytics Metrics Visualization Dashboards 96

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 98

Data Processing Data Warehouse Enterprise Visualization 98

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

Jet Global

MAY 1, 2023

that gathers data from many sources. 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. Ask your vendors for references. It’s all about context.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52