AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. Data engineers are crucial for schema conversion and data transformation, and DBAs can handle cluster configuration and workload monitoring. Platform architects define a well-architected platform.

Data Warehouse 115

Data Warehouse KPI Optimization Cost-Benefit 115

AWS Big Data

DECEMBER 21, 2023

As the volume and complexity of analytics workloads continue to grow, customers are looking for more efficient and cost-effective ways to ingest and analyse data. AWS Glue provides both visual and code-based interfaces to make data integration effortless. Select the secret you created, and on the Actions menu, choose Delete.

Analytics 111

Analytics IT Data Lake Visualization 111

AWS Big Data

FEBRUARY 8, 2023

It’s common to ingest multiple data sources into Amazon Redshift to perform analytics. Often, each data source will have its own processes of creating and maintaining data, which can lead to data quality challenges within and across sources. Answering questions as simple as “How many unique customers do we have?”

Data Quality 98

Data Quality Testing Data Warehouse Unstructured Data 98

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

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. So questions linger about whether transformed data can be trusted.

Data Governance 40

Data Governance Risk Metadata Management 40

DataKitchen

DECEMBER 9, 2022

Observability in DataOps refers to the ability to monitor and understand the performance and behavior of data-related systems and processes, and to use that information to improve the quality and speed of data-driven decision making. By using DataOps, organizations can improve. Query> When do DataOps?

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. This produces end-to-end lineage so business and technology users alike can understand the state of a data lake and/or lake house.

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

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 75

Cost-Benefit Metadata Optimization Management 75

Alation

AUGUST 11, 2022

Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. But there are only so many data engineers available in the market today; there’s a big skills shortage. Let’s take data privacy as an example.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

AWS Big Data

DECEMBER 4, 2024

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

Metadata 105

Metadata Data Governance Data Quality Data-driven 105

Ontotext

FEBRUARY 12, 2024

This is especially beneficial when teams need to increase data product velocity with trust and data quality, reduce communication costs, and help data solutions align with business objectives. In most enterprises, data is needed and produced by many business units but owned and trusted by no one.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

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 Data Quality 52

AWS Big Data

APRIL 5, 2023

“If each tool tells a different story because it has different data, we won’t have alignment within the business on what this data means.” The company also used the opportunity to reimagine its data pipeline and architecture.

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 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

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Visualization 52