CIO Business Intelligence

JUNE 29, 2022

The recent announcement of the Microsoft Intelligent Data Platform makes that more obvious, though analytics is only one part of that new brand. Azure Data Factory. Azure Data Explorer. Azure Data Lake Analytics. Data warehouses are designed for questions you already know you want to ask about your data, again and again.

Data Lake 116

Data Lake Analytics Data Warehouse Machine Learning 116

AWS Big Data

APRIL 30, 2024

Amazon Q Developer can now generate complex data integration jobs with multiple sources, destinations, and data transformations. Generated jobs can use a variety of data transformations, including filter, project, union, join, and custom user-supplied SQL. In his spare time, he enjoys cycling with his road bike.

Data Integration 105

Data Integration Data Lake Data Warehouse Software 105

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Extract, load, Transform (ELT) tools. Data ingestion/integration services. Data orchestration tools. Better Data Culture.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

AWS Big Data

NOVEMBER 15, 2023

The difference lies in when and where data transformation takes place. In ETL, data is transformed before it’s loaded into the data warehouse. In ELT, raw data is loaded into the data warehouse first, then it’s transformed directly within the warehouse.

Dashboards 97

Dashboards Analytics Metadata Data Warehouse 97

IBM Big Data Hub

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 87

Enterprise Data Warehouse Data Lake Optimization 87

Data Virtualization

JANUARY 20, 2021

Learn in 12 minutes: What makes a strong use case for data virtualisation How to come up with a solid Proof of Concept How to prepare your organisation for data virtualisation You’ll have read all about data virtualisation and you’ve.

Data Warehouse 59

Data Warehouse Data Architecture Data Transformation IT 59

AWS Big Data

OCTOBER 18, 2023

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. Spark SQL is an Apache Spark module for structured data processing. About the authors Amit Maindola is a Senior Data Architect focused on big data and analytics at Amazon Web Services.

Big Data 83

Big Data Data Processing Interactive Testing 83

FineReport

APRIL 21, 2024

Interactive dashboards , ad-hoc reporting, and big data connectivity are also essential for effective embedded analytics solutions. However, users note limitations in advanced analytics, customization, data transformation challenges, and issues with offline access and slow loading times for large datasets.

Dashboards 52

Dashboards Visualization Interactive Business Intelligence 52

Sisense

MAY 11, 2021

Here at Sisense, we think about this flow in five linear layers: Raw This is our data in its raw form within a data warehouse. We follow an ELT ( E xtract, L oad, T ransform) practice, as opposed to ETL, in which we opt to transform the data in the warehouse in the stages that follow. Dig into AI.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

IBM Big Data Hub

JULY 17, 2023

It is supported by querying, governance, and open data formats to access and share data across the hybrid cloud. Through workload optimization across multiple query engines and storage tiers, organizations can reduce data warehouse costs by up to 50 percent.

Machine Learning 65

Machine Learning Data Warehouse Modeling Cost-Benefit 65

IBM Big Data Hub

MARCH 14, 2024

. With Db2 Warehouse’s fully managed cloud deployment on AWS, enjoy no overhead, indexing, or tuning and automated maintenance. Whether it’s for ad hoc analytics, data transformation, data sharing, data lake modernization or ML and gen AI, you have the flexibility to choose.

Cost-Benefit 79

Cost-Benefit Metadata Optimization Management 79

AWS Big Data

MARCH 3, 2023

For these customers, Tricentis provides incremental data as Apache Parquet files and will have the ability to filter on specific projects and specific date ranges. To ensure data integrity, Tricentis uses its technology known as Tosca DI (not part of the AWS Data Lab session). You can follow his Twitter @simongui.

Software 52

Software Data Lake Testing Cost-Benefit 52

AWS Big Data

JUNE 29, 2023

Kinesis Data Analytics Studio uses Apache Zeppelin as the notebook, and uses Apache Flink as the stream processing engine. Kinesis Data Analytics Studio notebooks seamlessly combine these technologies to make advanced analytics on data streams accessible to developers of all skill sets. View the stream data.

Data Analytics 76

Data Analytics Analytics IoT Data Lake 76

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

Rocket-Powered Data Science

MARCH 20, 2023

With all the data in and around the enterprise, users would say that they have a lot of information but need more insights to assist them in producing better and more informative content. This is where we dispel an old “big data” notion (heard a decade ago) that was expressed like this: “we need our data to run at the speed of business.”

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Unstructured Data 325

AWS Big Data

MARCH 15, 2023

When designing the data processing pipeline for the attribute API, the Infomedia team wanted to use a flexible and open-source solution for processing data workloads with minimal operational overhead. The API retrieves data at runtime from an Amazon Aurora PostgreSQL-Compatible Edition database for end-user consumption.

Cost-Benefit 68

Cost-Benefit Data Processing Optimization Data-driven 68

IBM Big Data Hub

JUNE 15, 2023

It is comprised of commodity cloud object storage, open data and open table formats, and high-performance open-source query engines. To help organizations scale AI workloads, we recently announced IBM watsonx.data , a data store built on an open data lakehouse architecture and part of the watsonx AI and data platform.

Data Warehouse 91

Data Warehouse Data Lake Optimization Data-driven 91

AWS Big Data

SEPTEMBER 19, 2023

Chime is a financial technology company founded on the premise that basic banking services should be helpful, easy, and free. However, our legacy data warehouse-based solution was not equipped for this challenge. About the Authors Khandu Shinde is a Staff Engineer focused on Big Data Platforms and Solutions for Chime.

Analytics 81

Analytics Risk Big Data Machine Learning 81

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. CRM platforms).

Data Processing 92

Data Processing Data Warehouse Enterprise Visualization 92

IBM Big Data Hub

JULY 5, 2023

This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale. Another unexpected challenge was the introduction of Spark as a processing framework for big data.

Data Lake 63

Data Lake Metadata Cost-Benefit Data Warehouse 63

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

MAY 1, 2023

BI is a set of independent systems (technologies, processes, people, etc.) that gathers data from many sources. These tools prep that data for analysis and then provide reporting on it from a central viewpoint. And Manufacturing and Technology, both 11.6 It’s all about context. Financial Services represent 13.0

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52