Ontotext

AUGUST 24, 2023

The second approach is to use some Data Integration Platform. As an enterprise-supported tool, it has already established how to make all data transformations. Then the recommended approach is to use one of the many JSON to RDF transformation frameworks to produce RDF data.

Data Integration 52

Data Integration Modeling Business Objectives Optimization 52

erwin

APRIL 29, 2020

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. Business terms and data policies should be implemented through standardized and documented business rules.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

AWS Big Data

JULY 31, 2023

Customers often use many SQL scripts to select and transform the data in relational databases hosted either in an on-premises environment or on AWS and use custom workflows to manage their ETL. AWS Glue is a serverless data integration and ETL service with the ability to scale on demand. Choose Confirm.

Sales 52

Sales Data Warehouse Visualization Testing 52

Cloudera

JUNE 1, 2021

These acquisitions usher in a new era of “ self-service ” by automating complex operations so customers can focus on building great data-driven apps instead of managing infrastructure. Datacoral powers fast and easy data transformations for any type of data via a robust multi-tenant SaaS architecture that runs in AWS.

Uncertainty 144

Uncertainty Cost-Benefit Risk Strategy 144

IBM Big Data Hub

JANUARY 9, 2024

This may also entail working with new data through methods like web scraping or uploading. Data governance is an ongoing process in the data lifecycle to help ensure compliance with laws and company best practices. Data integration: These tools enable companies to combine disparate data sources into one secure location.

Enterprise 84

Enterprise Data Integration Data Quality Contextual Data 84

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on.

Data Governance 40

Data Governance Risk Metadata Management 40

AWS Big Data

OCTOBER 27, 2023

Data integration is the foundation of robust data analytics. It encompasses the discovery, preparation, and composition of data from diverse sources. In the modern data landscape, accessing, integrating, and transforming data from diverse sources is a vital process for data-driven decision-making.

Analytics 92

Analytics Visualization Data Integration Cost-Benefit 92

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

Alation

JANUARY 17, 2023

What if, experts asked, you could load raw data into a warehouse, and then empower people to transform it for their own unique needs? Today, data integration platforms like Rivery do just that. By pushing the T to the last step in the process, such products have revolutionized how data is understood and analyzed.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

Ontotext

MARCH 27, 2024

Poor data modeling capabilities of LPGs with vendor specific constructs to express semantic constraints hinders portability, expressibility, and semantic data integration. It accelerates data projects with data quality and lineage and contextualizes through ontologies , taxonomies, and vocabularies, making integrations easier.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

Sisense

AUGUST 6, 2020

As an AI product manager, here are some important data-related questions you should ask yourself: What is the problem you’re trying to solve? What data transformations are needed from your data scientists to prepare the data? What are the right KPIs and outputs for your product? What will it take to build your MVP?

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Alation

AUGUST 11, 2022

Everybody’s trying to solve this same problem (of leveraging mountains of data), but they’re going about it in slightly different ways. Data fabric is a technology architecture. It’s a data integration pattern that brings together different systems, with the metadata, knowledge graphs, and a semantic layer on top.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

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

Alation

OCTOBER 28, 2021

Too much access increases the risk that data can be changed or stolen. Remove Low Quality, Unused, or “Stale” Data. In healthcare especially, data integrity is incredibly important. Low quality, unused, or “stale” data can negatively impact research by skewing findings. Subscribe to Alation's Blog.

Data Governance 52

Data Governance Measurement Modeling Metrics 52

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 84

Data Lake Analytics Snapshot Optimization 84

Occam's Razor

OCTOBER 19, 2010

I was reflecting on that recently and thought it was incredible that in all my years of writing this blog I have never written a blog post, not one single one (!!), My goal is to give you a list of tools that I use in my everyday life as a practitioner (you'll see many of them implemented on this blog). Disclosure].

Analytics 135

Analytics Testing Measurement Optimization 135

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 99

Data Lake Management Metrics Data Warehouse 99

AWS Big Data

OCTOBER 5, 2023

In today’s data-driven world, the ability to effortlessly move and analyze data across diverse platforms is essential. Amazon AppFlow , a fully managed data integration service, has been at the forefront of streamlining data transfer between AWS services, software as a service (SaaS) applications, and now Google BigQuery.

Data Warehouse 82

Data Warehouse Machine Learning Data Integration Data-driven 82

Smarten

AUGUST 4, 2023

If your team has easy-to-use tools and features, you are much more likely to experience the user adoption you want and to improve data literacy and data democratization across the organization. Machine learning capability determines the best techniques, and the best fit transformations for data so that the outcome is clear and concise.

Data Lake 52

Data Lake Machine Learning Data Integration Optimization 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. This is something that you can learn more about in just about any technology blog.

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

erwin

MARCH 26, 2020

Reducing the IT bottleneck that creates barriers to data accessibility. Desire for self-service to free the data consumers from strict predefined data transformations and organizations. Hybrid on-premises/cloud environments that complicate data integration and preparation.

Data-driven 90

Data-driven Unstructured Data Enterprise Finance 90

DataKitchen

JANUARY 23, 2023

Testing data and analytic systems require a development system with accurate test data, tools, and relevant tool code. Only then can you tell the true impact of a column name change on the data transformations, the models, and the visualization you give to your customers. They measure data sets at a point in time.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Cloudera

NOVEMBER 22, 2021

Rise in polyglot data movement because of the explosion in data availability and the increased need for complex data transformations (due to, e.g., different data formats used by different processing frameworks or proprietary applications). As a result, alternative data integration technologies (e.g.,

Data Processing 88

Data Processing Data Warehouse Enterprise Visualization 88

Jet Global

MARCH 13, 2024

Between complex data structures, data security questions, and error-prone manual processes, merging data from disparate sources into a single system can quickly turn your routine reporting processes into a stressful and time-consuming ordeal. With Atlas, you can put your data security concerns to rest.

Reporting 52

Reporting Finance Data Quality Sales 52