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 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

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 119

Analytics Data Science Statistics Unstructured Data 119

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? One challenge in applying data science is to identify pertinent business issues.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

AWS Big Data

JANUARY 8, 2024

Stream ingestion – The stream ingestion layer is responsible for ingesting data into the stream storage layer. It provides the ability to collect data from tens of thousands of data sources and ingest in real time. The raw data can be streamed to Amazon S3 for archiving.

Analytics 112

Analytics IoT Data-driven Snapshot 112

datapine

NOVEMBER 19, 2019

In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data discovery tools available in the market to take their brand forward.

Business Intelligence 146

Business Intelligence Knowledge Discovery Dashboards Unstructured Data 146

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

AWS Big Data

MARCH 25, 2024

This data store provides your organization with the holistic customer records view that is needed for operational efficiency of RAG-based generative AI applications. For building such a data store, an unstructured data store would be best. This is typically unstructured data and is updated in a non-incremental fashion.

Data Lake 89

Data Lake Unstructured Data Management Modeling 89

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 112

Data Lake Analytics Dashboards Metrics 112

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

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 108

Data Lake Analytics Data Warehouse Machine Learning 108

FineReport

JUNE 24, 2021

Technicals such as data warehouse, online analytical processing (OLAP) tools, and data mining are often binding. On the opposite, it is more of a comprehensive application of data warehouse, OLAP, data mining, and so forth. BI software solutions often support multiple data source connections.

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

datapine

NOVEMBER 19, 2019

In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data Discovery tools available in the market to take their brand forward.

Business Intelligence 20

Business Intelligence Knowledge Discovery Unstructured Data Software 20

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Testing and Data Observability.

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 140

Data Lake Data Warehouse Unstructured Data Business Intelligence 140

Smart Data Collective

SEPTEMBER 26, 2021

Data mining and knowledge go hand in hand, providing insightful information to create applications that can make predictions, identify patterns, and, last but not least, facilitate decision-making. Working with massive structured and unstructured data sets can turn out to be complicated. If it’s not done right away, then later.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

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 97

Strategy Cost-Benefit Visualization Data Warehouse 97

BizAcuity

MAY 10, 2022

2007: Amazon launches SimpleDB, a non-relational (NoSQL) database that allows businesses to cheaply process vast amounts of data with minimal effort. An efficient big data management and storage solution that AWS quickly took advantage of. They now have a disruptive data management solution to offer to its client base.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

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

Sisense

MAY 28, 2019

Two orthogonal approaches to data analytics have developed in this decade of BI: 1. Operating “in-data” to enable the direct query of unstructured data lakes, providing a visualization layer on top of them. No single player is paying closer attention to this trend than the cloud vendors.

Data Analytics 89

Data Analytics Data Lake Analytics Unstructured Data 89

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

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 77

Data Collection Visualization Data-driven Unstructured Data 77

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

AWS Big Data

FEBRUARY 8, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. 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 76

Data Quality Testing Data Warehouse Unstructured Data 76

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

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. It can be used for something as visual as reducing traffic jams, to personalizing products and services, to improving the experience in multiplayer video games. We would like to talk about data visualization and its role in the big data movement.

Visualization 136

Visualization Cost-Benefit Big Data Prescriptive Analytics 136

datapine

FEBRUARY 22, 2022

Over the past 5 years, big data and BI became more than just data science buzzwords. Without real-time insight into their data, businesses remain reactive, miss strategic growth opportunities, lose their competitive edge, fail to take advantage of cost savings options, don’t ensure customer satisfaction… the list goes on.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Rocket-Powered Data Science

MARCH 20, 2023

Content includes reports, documents, articles, presentations, visualizations, video, and audio representations of the insights and knowledge that have been extracted from data. We could further refine our opening statement to say that our business users are too often in a state of being data-rich, but insights-poor, and content-hungry.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Unstructured Data 325

Cloudera

APRIL 1, 2024

This recognition underscores Cloudera’s commitment to continuous customer innovation and validates our ability to foresee future data and AI trends, and our strategy in shaping the future of data management. Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics.

Unstructured Data 105

Unstructured Data Cost-Benefit Metadata Machine Learning 105

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 93

Data Processing Data Warehouse Enterprise Visualization 93

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