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. Data is at the core of any ML project, so data infrastructure is a foundational concern. Data Science Layers. Model Development.

IT 351

IT Testing Experimentation Software 351

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. Data transformation – Steps 3 and 4 represent an EMR Serverless Spark application (Amazon EMR 6.9 Let’s refer to this S3 bucket as the raw layer. if len(tables)!=len(partition_keys):

Data Lake 83

Data Lake Dashboards Metrics Metadata 83

AWS Big Data

MARCH 3, 2023

Initially, Tricentis defines these dashboards and charts to enable insight on test runs, test traceability with requirements, and many other pre-defined use cases that can be valuable to customers. As the files are created, another process is triggered to load the data from each customer on their schema or table on Amazon Redshift.

Software 52

Software Data Lake Testing Cost-Benefit 52

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

To support these advanced use cases, data was structured in Cloud Data Teams by Sisense (formerly Periscope Data) to simplify SQL queries on dashboards by creating varying levels of granularities in tables visible to the tool. The right data model + artificial intelligence = augmented analytics. Dig into AI.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Jet Global

MAY 28, 2019

Whether the reporting is being done by an end user, a data science team, or an AI algorithm, the future of your business depends on your ability to use data to drive better quality for your customers at a lower cost. So, when it comes to collecting, storing, and analyzing data, what is the right choice for your enterprise?

Data Warehouse 85

Data Warehouse Reporting Business Intelligence Sales 85

Jet Global

OCTOBER 30, 2023

However, leveraging these insights can be challenging if your reports and data don’t speak to each other. Disconnected enterprise performance management (EPM) operational reporting can present significant limitations and challenges for your business. Transforming Financial Reporting with Dynamic Dashboards Download Now 1.

Dashboards 52

Dashboards Reporting Operational Reporting Software 52

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MAY 1, 2023

Their dashboards were visually stunning. In turn, end users were thrilled with the bells and whistles of charts, graphs, and dashboards. As rich, data-driven user experiences are increasingly intertwined with our daily lives, end users are demanding new standards for how they interact with their business data.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MARCH 13, 2024

Complex Data Structures and Integration Processes Dynamics data structures are already complex – finance teams navigating Dynamics data frequently require IT department support to complete their routine reporting.

Reporting 52

Reporting Finance Data Quality Sales 52