erwin

APRIL 29, 2020

These tools range from enterprise service bus (ESB) products, data integration tools; extract, transform and load (ETL) tools, procedural code, application program interfaces (API)s, file transfer protocol (FTP) processes, and even business intelligence (BI) reports that further aggregate and transform data.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

Sisense

FEBRUARY 26, 2020

In Transform to Win , we explore the challenges facing modern companies, diving into their individual digital transformations and the people who drive them. We all know that data is becoming more and more essential for businesses, as the volume of data keeps growing.

Digital Transformation 90

Digital Transformation Analytics Cost-Benefit Data-driven 90

Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Extract, load, Transform (ELT) tools. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. How Did the Modern Data Stack Get Started? How Can I Build a Modern Data Stack?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

Cloudera

AUGUST 17, 2021

When we announced the GA of Cloudera Data Engineering back in September of last year, a key vision we had was to simplify the automation of data transformation pipelines at scale. Let’s take a common use-case for Business Intelligence reporting. Figure 2: Example BI reporting data pipeline.

Management 111

Management Cost-Benefit Data Transformation Optimization 111

Sisense

DECEMBER 12, 2019

Building a data-driven business includes choosing the right software and implementing best practices around its use. Our Business Perspectives help you make smarter decisions no matter where you are in your analytics journey. No competitive business is still using Excel to crunch large, scattered and unstructured data sets?

Business Intelligence 88

Business Intelligence Software Data-driven Visualization 88

Cloudera

JUNE 30, 2022

These tools empower analysts and data scientists to easily collaborate on the same data, with their choice of tools and analytic engines. No more lock-in, unnecessary data transformations, or data movement across tools and clouds just to extract insights out of the data.

Data Lake 84

Data Lake Data Architecture Metadata Data Warehouse 84

IBM Big Data Hub

JANUARY 18, 2023

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. A read-optimized platform that can integrate data from multiple applications emerged.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

IBM Big Data Hub

MARCH 14, 2024

.   Request a live demo or start a proof of concept with Amazon RDS for Db2 Db2 Warehouse SaaS on AWS The cloud-native Db2 Warehouse fulfills your price and performance objectives for mission-critical operational analytics, business intelligence (BI) and mixed workloads.

Cost-Benefit 83

Cost-Benefit Metadata Optimization Management 83

IBM Big Data Hub

JULY 17, 2023

IBM software products are embedding watsonx capabilities across digital labor, IT automation, security, sustainability, and application modernization to help unlock new levels of business value for clients. In this blog, I will cover: What is watsonx.ai? ” Vitaly Tsivin, EVP Business Intelligence at AMC Networks.

Machine Learning 67

Machine Learning Data Warehouse Modeling Cost-Benefit 67

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. The right data model + artificial intelligence = augmented analytics. Dig into AI.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

AWS Big Data

FEBRUARY 8, 2023

Amazon Redshift enables you to run complex SQL analytics at scale and performance on terabytes to petabytes of structured and unstructured data, and make the insights widely available through popular business intelligence (BI) and analytics tools.

Data Quality 80

Data Quality Testing Data Warehouse Unstructured Data 80

AWS Big Data

JULY 20, 2023

This data is then used by various applications for streaming analytics, business intelligence, and reporting. This hands-on experience will provide valuable insights into the complexities of a transactional data lake. Amazon SageMaker is used to build, train, and deploy a range of ML models.

Data Lake 90

Data Lake Analytics Snapshot Optimization 90

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 108

Data Lake Management Metrics Data Warehouse 108

AWS Big Data

AUGUST 1, 2023

Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other business data, as well as support the use of business intelligence (BI) tools and artificial intelligence (AI) and machine learning (ML) applications. Choose Update.

Data Lake 88

Data Lake Data Transformation Cost-Benefit Data-driven 88

AWS Big Data

SEPTEMBER 19, 2023

However, our legacy data warehouse-based solution was not equipped for this challenge. It was designed to manage complex queries and business intelligence (BI) use cases on a large scale. The AWS Glue streaming jobs generate derived fields and risk profiles that get stored in Amazon DynamoDB.

Analytics 83

Analytics Risk Big Data Machine Learning 83