DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. Overall, DataOps is an essential component of modern data-driven organizations. Query> Write an essay on DataOps Automation.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

Smart Data Collective

APRIL 29, 2020

What is data management? Data management can be defined in many ways. The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated.

Management 83

Management Data Warehouse Digital Transformation Dashboards 83

Ontotext

NOVEMBER 18, 2021

You step onto the market, and if you don’t keep your data, there’s no knowing where you might be swept off to. [1]. Picture this – you start with the perfect use case for your data analytics product. Nowadays, data analytics doesn’t exist on its own. You make a great pitch and you sell well.

Visualization 98

Visualization Reporting Metadata Enterprise 98

Sisense

OCTOBER 15, 2020

We live in a world of data: There’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways data teams are tackling the challenges of this new world to help their companies and their customers thrive. Cleaning up dirty data.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

CIO Business Intelligence

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

Analytics 130

Analytics Machine Learning Software Data Quality 130

AWS Big Data

JULY 20, 2023

The Orca Platform is powered by a state-of-the-art anomaly detection system that uses cutting-edge ML algorithms and big data capabilities to detect potential security threats and alert customers in real time, ensuring maximum security for their cloud environment. Why did Orca choose Apache Iceberg?

Data Lake 89

Data Lake Analytics Snapshot Optimization 89

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

Cost-Benefit 79

Cost-Benefit Metadata Optimization Management 79

AWS Big Data

MARCH 3, 2023

Teams may struggle to have a unified view of software quality due to siloed testing across many disparate tools. Although Tricentis has amassed such data over a decade, the data remains untapped for valuable insights. This is a guest post co-written with Parag Doshi, Guru Havanur, and Simon Guindon from Tricentis.

Software 52

Software Data Lake Testing Cost-Benefit 52

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. We would like to talk about data visualization and its role in the big data movement.

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

DataKitchen

JANUARY 23, 2023

What is data lineage? Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. It is used to understand the provenance of data and how it is transformed and to identify potential errors or issues. What is missing in data lineage?

Testing 130

Testing Data Governance Data Quality Data-driven 130

Ontotext

FEBRUARY 12, 2024

For large enterprises, data mesh distributes data ownership and reduces dependencies between services. This promotes data autonomy and enables decision-making about data domains without centralized gatekeepers. What does data mesh do that other approaches can’t?

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

Alation

NOVEMBER 22, 2022

Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements. With so much focus on compliance, democratizing data for self-service analytics can present a challenge. Scattered Data. Changing Regulations.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

Jet Global

MAY 9, 2024

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization. What are examples of data pipelines?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52