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 121

Data Lake Management Metrics Data Warehouse 121

DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. Azure Blob Storage serves as the data lake to store raw data.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

AWS Big Data

AUGUST 19, 2024

As organizations increasingly rely on data stored across various platforms, such as Snowflake , Amazon Simple Storage Service (Amazon S3), and various software as a service (SaaS) applications, the challenge of bringing these disparate data sources together has never been more pressing.

Analytics 118

Analytics Data-driven Data Integration Data Lake 118

AWS Big Data

NOVEMBER 7, 2023

For these, AWS Glue provides fast, scalable data transformation. Third, AWS continues adding support for more data sources including connections to software as a service (SaaS) applications, on-premises applications, and other clouds so organizations can act on their data. Visit Data integration with AWS to learn more.

Dashboards 79

Dashboards Data-driven Data Integration Data Lake 79

Sisense

AUGUST 6, 2020

An obvious mechanical answer is: use relevance as a metric. Another important method is to benchmark existing metrics. Know the limitations of your existing dataset and answer these questions: What categories of data are there? What data transformations are needed from your data scientists to prepare the data?

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Wayne Yaddow

MARCH 3, 2025

These strategies minimize risks, streamline deployment processes, and future-proof data transformations, allowing businesses to trust their data before it ever reaches production. Helps maintain business rule consistency and avoid regressions in data quality overtime. Summary: Why thisorder?

Data Quality 52

Data Quality Strategy ROI Testing 52

Octopai

NOVEMBER 13, 2022

The modern data stack is a data management system built out of cloud-based data systems. A given modern data stack will usually include components for data ingestion from your data sources, data transformation, data storage, data analysis and reporting.

Enterprise 97

Enterprise Data Warehouse Reporting Metadata 97

DataKitchen

OCTOBER 25, 2022

It’s because it’s a hard thing to accomplish when there are so many teams, locales, data sources, pipelines, dependencies, data transformations, models, visualizations, tests, internal customers, and external customers. You can’t quality-control your data integrations or reports with only some details. .

Testing 130

Testing Data-driven Visualization Dashboards 130

AWS Big Data

JULY 20, 2023

Specifically, the system uses Amazon SageMaker Processing jobs to process the data stored in the data lake, employing the AWS SDK for Pandas (previously known as AWS Wrangler) for various data transformation operations, including cleaning, normalization, and feature engineering.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

Octopai

JUNE 9, 2024

For instance, aligning patient care data from Oracle databases with operational metrics from Power BI was daunting without clear data lineage. Different departments managed their data independently, leading to silos and inconsistencies. This led to better integration and consistency across the organization.

IT 52

IT Data-driven Predictive Analytics Data Strategy 52

AWS Big Data

MARCH 3, 2023

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

Alation

OCTOBER 28, 2021

To make good on this potential, healthcare organizations need to understand their data and how they can use it. This means establishing and enforcing policies and processes, standards, roles, and metrics. Why Is Data Governance in Healthcare Important? Too much access increases the risk that data can be changed or stolen.

Data Governance 52

Data Governance Measurement Data Quality Metrics 52

Occam's Razor

OCTOBER 19, 2010

Mongoose Metrics ~ ifbyphone. I know Mongoose Metrics a bit more and have been impressed with their solution and evolution over the last couple of years. Twitter to me is a proxy of how data collection is changing and what the future of relevant metrics might look like. Mongoose Metrics. AnalyzeWords. LivePerson.

Analytics 136

Analytics Testing Measurement Optimization 136

Jet Global

MAY 21, 2025

Unlocking the full potential of your data is about more than just visualizing it. True data transformation comes from applying insights to make impactful business decisions. They provide a clear view of metrics and KPIs and give stakeholders a good overview of the organizations health.

Dashboards 52

Dashboards Visualization Reporting Interactive 52

Jet Global

MAY 1, 2023

As a result, end users can better view shared metrics (backed by accurate data), which ultimately drives performance. When treating a patient, a doctor may wish to study the patient’s vital metrics in comparison to those of their peer group. Visual Analytics Users are given data from which they can uncover new insights.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

MAY 9, 2024

Data Extraction : The process of gathering data from disparate sources, each of which may have its own schema defining the structure and format of the data and making it available for processing. 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

DataKitchen

JANUARY 23, 2023

On the other hand, DataOps Observability refers to understanding the state and behavior of data as it flows through systems. It allows organizations to see how data is being used, where it is coming from, and how it is being transformed. Data lineage does not directly improve data quality.

Testing 130

Testing Data Governance Data Quality Data-driven 130