AWS Big Data

NOVEMBER 29, 2023

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

Data Warehouse 103

Data Warehouse Data Lake Analytics Machine Learning 103

Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

AWS Big Data

MARCH 26, 2024

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Then, you transform this data into a concise format.

Data Strategy 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

erwin

JULY 17, 2019

Your organization won’t be able to take complete advantage of analytics tools to become data-driven unless you establish a foundation for agile and complete data management. You need automated data mapping and cataloging through the integration lifecycle process, inclusive of data at rest and data in motion.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

DataKitchen

SEPTEMBER 21, 2023

What is Data in Place? Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. By diligently testing and monitoring data in Use, you uphold data integrity and provide tangible value to end-users.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping helps standardize, visualize, and understand data across different systems and applications.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

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 105

Data Lake Management Metrics Data Warehouse 105

AWS Big Data

MARCH 15, 2023

They wanted to develop a simple incremental data processing pipeline without having to update the entire database each time the pipeline ran. The Apache Hudi framework allowed the Infomedia team to maintain a golden reference dataset and capture changes so that the downstream database could be incrementally updated in a short timeframe.

Cost-Benefit 68

Cost-Benefit Data Processing Optimization Data-driven 68

AWS Big Data

FEBRUARY 1, 2023

Architecturally, we chose a serverless model, and the data lake architecture action line refers to all the architectural gaps and challenging features we determined were part of the improvements. We also used AWS Lambda for data processing. To query data from REST APIs and other data sources, we used PySpark and JDBC modules.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

Ontotext

FEBRUARY 26, 2024

Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. They have massive amounts of data they’re collecting and storing in their relational databases, document stores, data lakes, and data warehouses. RDF vs LPG?

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction.

Data Processing 92

Data Processing Data Warehouse Enterprise Visualization 92

erwin

MAY 2, 2022

Data modeling is a serious scientific method with many rules and best practices. One must also capture the vast quantity of metadata around the OLTP business requirements that must be reflected. I was pricing a data warehousing project with just 4 TB of data – small by today’s standards.

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

Domino Data Lab

APRIL 3, 2019

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Those days are long gone if they ever existed.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. Data ingestion/integration services. Reverse ETL tools.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

Alation

JANUARY 25, 2022

As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. In this blog, we will focus on the “integrated layer” part of this definition by examining each of the key layers of a comprehensive data fabric in more detail.

Metadata 52

Metadata IT Metrics Data-driven 52

Jet Global

MAY 1, 2023

that gathers data from many sources. These sit on top of data warehouses that are strictly governed by IT departments. The role of traditional BI platforms is to collect data from various business systems. Metadata Self-service analysis is made easy with user-friendly naming conventions for tables and columns.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

AWS Big Data

DECEMBER 18, 2023

Amazon Redshift powers data-driven decisions for tens of thousands of customers every day with a fully managed, AI-powered cloud data warehouse, delivering the best price-performance for your analytics workloads. Discover how you can use Amazon Redshift to build a data mesh architecture to analyze your data.

Data Warehouse 100

Data Warehouse Machine Learning Data-driven Data Lake 100

IBM Big Data Hub

AUGUST 4, 2023

In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62