CIO Business Intelligence

OCTOBER 13, 2023

Data architect role Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles, often in support of data or digital transformations. In some ways, the data architect is an advanced data engineer.

Data Architecture 130

Data Architecture Data Warehouse Statistics Visualization 130

IBM Big Data Hub

SEPTEMBER 19, 2023

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

Data Science 80

Data Science Data Analytics Prescriptive Analytics Analytics 80

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

Cloudera

APRIL 1, 2024

We scored the highest in hybrid, intercloud, and multi-cloud capabilities because we are the only vendor in the market with a true hybrid data platform that can run on any cloud including private cloud to deliver a seamless, unified experience for all data, wherever it lies.

Unstructured Data 104

Unstructured Data Cost-Benefit Metadata Machine Learning 104

CIO Business Intelligence

JUNE 29, 2022

Taking the broadest possible interpretation of data analytics , Azure offers more than a dozen services — and that’s before you include Power BI, with its AI-powered analysis and new datamart option , or governance-oriented approaches such as Microsoft Purview. Azure Data Factory. Azure Data Lake Analytics.

Data Lake 113

Data Lake Analytics Data Warehouse Machine Learning 113

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Testing 300

Testing Machine Learning Consulting Data Quality 300

CIO Business Intelligence

AUGUST 18, 2022

Traditionally, organizations have maintained two systems as part of their data strategies: a system of record on which to run their business and a system of insight such as a data warehouse from which to gather business intelligence (BI). You can intuitively query the data from the data lake.

Data Lake 139

Data Lake Data Warehouse Unstructured Data Business Intelligence 139

Jet Global

SEPTEMBER 4, 2020

For more sophisticated multidimensional reporting functions, however, a more advanced approach to staging data is required. The Data Warehouse Approach. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

CIO Business Intelligence

AUGUST 9, 2022

Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved. Data engineer job description.

Analytics 127

Analytics Data Science Statistics Unstructured Data 127

FineReport

JULY 28, 2021

Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information. Therefore, the learning curve will be steeper.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

Sisense

JULY 22, 2020

They hold structured data from relational databases (rows and columns), semi-structured data ( CSV , logs, XML , JSON ), unstructured data (emails, documents, PDFs), and binary data (images, audio , video). Sisense provides instant access to your cloud data warehouses. Connect tables.

Slice and Dice 98

Slice and Dice Digital Transformation Data Warehouse Data Lake 98

IBM Big Data Hub

SEPTEMBER 19, 2022

One way to achieve real-time analytics is with a combination of a time-series database (InfluxDB or TimescaleDB) or a NoSQL database (MongoDB) + a data warehouse + a BI tool: This architecture raises a question: Why would one use an operational database, and still need a data warehouse ? But wait, it gets better….

IoT 52

IoT Analytics Data Warehouse Manufacturing 52

Cloudera

AUGUST 20, 2021

Analytical Outcome: CDP delivers multiple analytical outcomes including, to name a few, operational dashboards via the CDP Operational Database experience or ad-hoc analytics via the CDP Data Warehouse to help surface insights related to a business domain. A Holistic Visual Exploration of Data.

Strategy 96

Strategy Cost-Benefit Visualization Data Warehouse 96

IBM Big Data Hub

MAY 17, 2023

The services are activated through access management for data collection, analysis and event monitoring in existing drones which are managed by clients and businesses. The flexibility of DaaS in offering a multiplicity of data collection services for different industry use cases makes it unique.

Data Collection 80

Data Collection Visualization Data-driven Unstructured Data 80

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This data store provides your organization with the holistic customer records view that is needed for operational efficiency of RAG-based generative AI applications. versions).

Data Lake 92

Data Lake Unstructured Data Management Modeling 92

Jedox

OCTOBER 22, 2020

In addition to using data to inform your future decisions, you can also use current data to make immediate decisions. Some of the technologies that make modern data analytics so much more powerful than they used t be include data management, data mining, predictive analytics, machine learning and artificial intelligence.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

BizAcuity

MAY 10, 2022

2012: Amazon Redshift, the first of its kind cloud-based data warehouse service comes into existence. Fact: IBM built the world’s first data warehouse in the 1980’s. Microsoft also releases Power BI, a data visualization and business intelligence tool. There is Alibaba Cloud, Turbonomic, Terremark etc.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

Sisense

OCTOBER 6, 2020

As quantitative data is always numeric, it’s relatively straightforward to put it in order, manage it, analyze it, visualize it, and do calculations with it. Spreadsheet software like Excel, Google Sheets, or traditional database management systems all mainly deal with quantitative data.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

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

AWS Big Data

MARCH 7, 2024

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 115

Data Lake Analytics Dashboards Metrics 115

Sisense

JULY 6, 2021

This cutting-edge service integrates the abilities of a data lake, a data warehouse, and purpose-built stores, to enable unified governance and easy data movement. To drive this point home, Yonatan Dolan, an Analytics Specialist from AWS, introduced AWS’ new Lake House architecture.

Data Lake 52

Data Lake Data Warehouse Data-driven Unstructured Data 52

Sisense

MARCH 14, 2021

Before we dive into the topics of big data as a service and analytics applied to same, let’s quickly clarify data analytics using an oft-used application of analytics: Visualization! As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data. (ESB

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

IBM Big Data Hub

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Sisense

MAY 11, 2021

Data teams dealing with larger, faster-moving cloud datasets needed more robust tools to perform deeper analyses and set the stage for next-level applications like machine learning and natural language processing. SkullCandy’s big data journey began by building a data warehouse to aggregate their transaction data, reviews.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Cloudera

MAY 16, 2018

All three cases require a “big picture” approach that incorporates new and alternative data sources and cross-functional collaboration throughout the organization not only to identify illegal activities, rogue traders, or personal misconduct but also to provide evidential material that demonstrates a deep understanding of the intent.

Data Lake 42

Data Lake Risk Visualization Unstructured Data 42