Smart Data Collective

APRIL 29, 2020

What is data management? Data management can be defined in many ways. Usually the term refers to the practices, techniques and tools that allow access and delivery through different fields and data structures in an organisation. Data transformation. Data analytics and visualisation. SharePoint.

Management 83

Management Data Warehouse Digital Transformation Dashboards 83

AWS Big Data

MARCH 16, 2023

Amazon Redshift is a fully managed data warehouse service that tens of thousands of customers use to manage analytics at scale. Together with price-performance , Amazon Redshift enables you to use your data to acquire new insights for your business and customers while keeping costs low.

Data Warehouse 52

Data Warehouse Testing Snapshot Modeling 52

FineReport

APRIL 21, 2024

In summary, embedded analytics refers to actionable intelligence seamlessly integrated into customer-facing products, applications, or services. Lengthy Turnaround Time In the competitive landscape of analytics, swift delivery of insights is paramount to proving the value of data and analytics teams.

Dashboards 52

Dashboards Visualization Interactive Business Intelligence 52

IBM Big Data Hub

OCTOBER 16, 2023

AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Enable responsible, transparent and explainable data and AI workflows with watsonx.governance.

Risk 70

Risk Modeling Management Metadata 70

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

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 66

Cost-Benefit Data Processing Optimization Data-driven 66

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 83

Data Lake Dashboards Metrics Metadata 83

AWS Big Data

MAY 25, 2023

Data transformation plays a pivotal role in providing the necessary data insights for businesses in any organization, small and large. To gain these insights, customers often perform ETL (extract, transform, and load) jobs from their source systems and output an enriched dataset.

Reporting 69

Reporting Metrics Optimization Data Lake 69

AWS Big Data

JUNE 29, 2023

Kinesis Data Analytics for Apache Flink In our example, we perform the following actions on the streaming data: Connect to an Amazon Kinesis Data Streams data stream. View the stream data. Transform and enrich the data. Manipulate the data with Python. Open the file to inspect the new data.

Data Analytics 74

Data Analytics Analytics IoT Data Lake 74

Sisense

MAY 11, 2021

Data teams dealing with larger, faster-moving cloud datasets needed more robust tools to perform deeper analyses and set the stage for next-level applications like machine learning and natural language processing. Here at Sisense, we think about this flow in five linear layers: Raw This is our data in its raw form within a data warehouse.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Jet Global

MAY 1, 2023

that gathers data from many sources. Their dashboards were visually stunning. In turn, end users were thrilled with the bells and whistles of charts, graphs, and dashboards. Yes—but basic dashboards won’t be enough. These users interact with dashboards and reports as well as personalized views of the information.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

O'Reilly on Data

OCTOBER 19, 2021

If you ask an engineer to show how they operate the application in production, they will likely show containers and operational dashboards—not unlike any other software service. but to reference concrete tooling used today in order to ground what could otherwise be a somewhat abstract exercise. Foundational Infrastructure Layers.

IT 351

IT Testing Experimentation Software 351