AWS Big Data

MARCH 16, 2023

A source of unpredictable workloads is dbt Cloud , which SafetyCulture uses to manage data transformations in the form of models. Whenever models are created or modified, a dbt Cloud CI job is triggered to test the models by materializing the models in Amazon Redshift. Refer to Connect dbt Cloud to Redshift for setup steps.

Data Warehouse 52

Data Warehouse Testing Snapshot Modeling 52

AWS Big Data

JULY 26, 2023

To grow the power of data at scale for the long term, it’s highly recommended to design an end-to-end development lifecycle for your data integration pipelines. The following are common asks from our customers: Is it possible to develop and test AWS Glue data integration jobs on my local laptop?

Data Integration 70

Data Integration Snapshot Testing Visualization 70

AWS Big Data

NOVEMBER 13, 2023

Consequently, there was a fivefold rise in data integrations and a fivefold increase in ad hoc queries submitted to the Redshift cluster. Also, over time the number of BI dashboards (both scheduled and live) increased, which contributed to more queries being submitted to the Redshift cluster.

Data Warehouse 90

Data Warehouse Data Lake Analytics Data Science 90

AWS Big Data

NOVEMBER 16, 2023

Building a starter version of anything can often be straightforward, but building something with enterprise-grade scale, security, resiliency, and performance typically requires knowledge of and adherence to battle-tested best practices, and using the right tools and features in the right scenario. Data Vault 2.0

Enterprise 83

Enterprise Data Warehouse Data Lake Optimization 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 67

Reporting Metrics Optimization Data Lake 67

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

JUNE 29, 2023

In this post, we discuss why AWS recommends moving from Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics for Apache Flink to take advantage of Apache Flink’s advanced streaming capabilities. View the stream data. Transform and enrich the data. Manipulate the data with Python.

Data Analytics 73

Data Analytics Analytics IoT Data Lake 73

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

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. The applications must be integrated to the surrounding business systems so ideas can be tested and validated in the real world in a controlled manner.

IT 346

IT Testing Experimentation Software 346

O'Reilly on Data

JANUARY 17, 2023

” I, thankfully, learned this early in my career, at a time when I could still refer to myself as a software developer. Upload your data, click through a workflow, walk away. If you’re a professional data scientist, you already have the knowledge and skills to test these models. It does not exist in the code.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

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. An example of a data science tool is Dataiku. How Can I Build a Modern Data Stack?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

Alation

OCTOBER 28, 2021

Protect data at the source. Put data into action to optimize the patient experience and adapt to changing business models. What is Data Governance in Healthcare? Data governance in healthcare refers to how data is collected and used by hospitals, pharmaceutical companies, and other healthcare organizations and service providers.

Data Governance 52

Data Governance Measurement Modeling Metrics 52