Datavail

JULY 7, 2022

Meanwhile, in an informal survey of attendees at a recent Datavail webinar, the majority (75 percent) of attendees said that their organization was pursuing a “hybrid” (partly on-premises and partly in the cloud) strategy for business intelligence and analytics. High data volumes. Artificial intelligence and machine learning.

Analytics 52

Analytics Artificial Inteligence Cloud Artificial Intelligence 52

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. Customers demanded more from the start.

Data 92

Data Analytics Open Source Architecture 92

Altexsoft

JUNE 14, 2021

This is possible because their machine learning model is retrained almost daily. On top of that, the company uses big data analytics to quantify losses and predict risks by placing the client into a risk group and quoting a relevant premium. Your vision on personalization may not work for every business model.

Insurance 88

Insurance Machine Learning Artificial Inteligence Policies 88

Apiumhub

MARCH 16, 2021

With more and more data available, it’s getting more difficult to focus on the information we really need and present it in an actionable way and that’s what business intelligence is all about. In this article we will talk about Business Intelligence tools, benefits & use cases. . What is Business Intelligence.

Business Intelligence 118

Business Intelligence Tools Artificial Inteligence Technical Review 118

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics.

Data 82

Data Analytics Cloud Technical Review 82

Datavail

MARCH 24, 2020

Loosely defined, a data strategy is a list of high-level objectives and priorities for your enterprise data assets. This includes your plans for handling data at every stage of the pipeline, from collection and storage to integration, analysis, and collaboration.

Strategy 40

Strategy Data Policies Storage 40

Altexsoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics. Big data processing.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Infinidat

MARCH 10, 2021

Re-Thinking the Storage Infrastructure for Business Intelligence. With digital transformation under way at most enterprises, IT management is pondering how to optimize storage infrastructure to best support the new big data analytics focus. Adriana Andronescu. Wed, 03/10/2021 - 12:42.

Business Intelligence 98

Business Intelligence Storage Infrastructure Artificial Inteligence 98

Altexsoft

JUNE 26, 2023

While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Altexsoft

AUGUST 19, 2022

In our blog, we’ve been talking a lot about the importance of business intelligence (BI), data analytics, and data-driven culture for any company. Users can easily create a wide range of data-intensive, yet intelligible reports and dashboards and share obtained insights. What is Power used for?

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

Altexsoft

SEPTEMBER 9, 2022

In data science , metadata is one of the central aspects: It describes data (including unstructured data streams) fed into a big data analytical platform, capturing, for example, formats, file sizes, source of information, permission details, etc. Metadata storage. Types of metadata.

Tools 59

Tools Technical Review Software Review Systems Review 59

Altexsoft

JULY 29, 2022

Depending on how you measure it, the answer will be 11 million newspaper pages or… just one Hadoop cluster and one tech specialist who can move 4 terabytes of textual data to a new location in 24 hours. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics.

Big Data 59

Big Data Data Google Cloud Storage 59

CIO

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure. Pulling it all together.

Artificial Inteligence 298

Artificial Inteligence Data Artificial Intelligence Analytics 298

TechCrunch

JUNE 25, 2021

He acknowledges that traditional big data warehousing works quite well for business intelligence and analytics use cases. But that’s not real-time and also involves moving a lot of data from where it’s generated to a centralized warehouse. That whole model is breaking down.”

Machine Learning 210

Machine Learning Artificial Inteligence Big Data Business Intelligence 210

Altexsoft

JANUARY 22, 2020

Please note: this topic requires some general understanding of analytics and data engineering, so we suggest you read the following articles if you’re new to the topic: Data engineering overview. A complete guide to business intelligence and analytics. The role of business intelligence developer.

Analytics 20

Analytics Data IoT Analysis 20

Altexsoft

APRIL 22, 2020

In its core, data science is all about getting data for analysis to produce meaningful and useful insights. The data can be further applied to provide value for machine learning , data stream analysis , business intelligence , or any other type of analytics. Or they can use no storage at all.

Data Engineering 69

Data Engineering Engineering Artificial Inteligence Data 69

Altexsoft

MAY 14, 2021

Big Data enjoys the hype around it and for a reason. But the understanding of the essence of Big Data and ways to analyze it is still blurred. This post will draw a full picture of what Big Data analytics is and how it works. Big Data and its main characteristics. Key Big Data characteristics.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

OCTOBER 30, 2021

As the complexity of tasks and the volume of data needed to process increased, data scientists started focusing more on helping businesses solve problems. Data scientists today are business-oriented analysts who know how to shape data into answers, often building complex machine learning models.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

Altexsoft

APRIL 23, 2019

But how to turn unstructured data chunks into something useful? The answer is business intelligence. We’ve already discussed a machine learning strategy. In this article, we will discuss the actual steps of bringing business intelligence into your existing corporate infrastructure.

Business Intelligence 93

Business Intelligence Analytics Tools Strategy 93

O'Reilly Media - Ideas

SEPTEMBER 19, 2023

It progressed from “raw compute and storage” to “reimplementing key services in push-button fashion” to “becoming the backbone of AI work”—all under the umbrella of “renting time and storage on someone else’s computers.” Those algorithms packaged with scikit-learn?

Data 111

Data Artificial Inteligence Machine Learning Hardware 111

Altexsoft

APRIL 13, 2023

Due to extensive usage of connected IoT devices and advanced processing technologies, SCCTs not only gather data and build operational reports but also create predictions, define the impact of various macro- and microeconomic factors on the supply chain, and run “what-if” scenarios to find the best course of action.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Kaseya

NOVEMBER 9, 2023

With the cloud, users and organizations can access the same files and applications from almost any device since the computing and storage take place on servers in a data center instead of locally on the user device or in-house servers. The servers ensure an efficient allocation of computing resources to support diverse user needs.

Cloud 105

Cloud Disaster Recovery Artificial Inteligence Infrastructure 105

O'Reilly Media - Data

MAY 3, 2018

Once we have data securely in place, we proceed to utilize it in two main ways: (1) to make better decisions (BI) and (2) to enable some form of automation (ML). Business intelligence and analytics. Machine learning. In multi-task learning, the goal is to consider fitting separate but related models simultaneously.

Analytics 127

Analytics Artificial Inteligence Data Machine Learning 127

Altexsoft

NOVEMBER 10, 2021

A data lakehouse , as the name suggests, is a new data architecture that merges data warehouse and data lake into a single whole, aiming at addressing each one’s limitations. In a nutshell, the lakehouse system leverages low-cost storage to keep large volumes of data in its raw formats just like data lakes.

Architecture 52

Architecture Data Storage Machine Learning 52

Altexsoft

APRIL 26, 2022

Not long ago setting up a data warehouse — a central information repository enabling business intelligence and analytics — meant purchasing expensive, purpose-built hardware appliances and running a local data center. The data journey from different source systems to a warehouse commonly happens in two ways — ETL and ELT.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

MARCH 18, 2021

From the late 1980s, when data warehouses came into view, and up to the mid-2000s, ETL was the main method used in creating data warehouses to support business intelligence (BI). As data keeps growing in volumes and types, the use of ETL becomes quite ineffective, costly, and time-consuming. Data size and type.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Altexsoft

MAY 20, 2020

These hardware components cache and preprocess real-time data, reducing the burden on central storages and main processors. The transport layer is responsible for smooth and secure data transmission from a perception to processing layer. Devices are connected to sensors or even have them embedded as an integral part.

IoT 141

IoT Azure AWS Transportation 141

Altexsoft

FEBRUARY 10, 2021

New approaches arise to speed up the transformation of raw data into useful insights. Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. The core difference lies in the point where raw data is transformed.

Analytics 75

Analytics DevOps Development Software Review 75

Kentik

FEBRUARY 14, 2023

What are the main features of a modern data platform? Data platforms offer enterprises a range of features: Data ingestion Data storage Data transformation Data modeling Data discovery Data observability Data security Business intelligence Data platform ingestion Useful data is generated at every layer of an application.

Network 52

Network Data Artificial Inteligence Analysis 52

Altexsoft

DECEMBER 14, 2020

As long as data fits within the structure of RDBMSs, it allows one to easily search for particular information and single out the relationships between its pieces. Such data can only be used for its intended purpose. On top of that, structured data doesn’t normally require much storage space. OLAP applications.

Data 93

Data Artificial Inteligence Storage Analytics 93

Existek

JANUARY 27, 2023

For example, many financial institutions are now using artificial intelligence and machine learning to analyze customer data and identify new opportunities for growth. Emerging technologies such as blockchain, AI, and machine learning are also becoming increasingly important in financial services software development.

Software Development 52

Software Development Trends Technology Software 52

Altexsoft

SEPTEMBER 23, 2021

Yet, more than often, businesses can’t make use of their most valuable asset — information. Evidently, common storage solutions fail to provide a unified data view and meet the needs of companies for seamless data flow. What is Data Hub? Data warehouse vs data hub.

Architecture 52

Architecture Data Storage IoT 52

Altexsoft

JANUARY 14, 2021

Snowflake, Redshift, BigQuery, and Others: Cloud Data Warehouse Tools Compared. From simple mechanisms for holding data like punch cards and paper tapes to real-time data processing systems like Hadoop, data storage systems have come a long way to become what they are now. Is it still so?

Backup 115

Backup Azure Software Review Systems Review 115

Cloudera

NOVEMBER 2, 2020

Correlations across data domains, even if they are not traditionally stored together (e.g. real-time customer event data alongside CRM data; network sensor data alongside marketing campaign management data). The extreme scale of “big data”, but with the feel and semantics of “small data”.

Data 95

Data Analytics Storage Big Data 95

ParkMyCloud

MAY 29, 2019

As more and more enterprises drive value from container platforms, infrastructure-as-code solutions, software-defined networking, storage, continuous integration/delivery, and AI, they need people and skills on board with ever more niche expertise and deep technological understanding. Business Intelligence Analyst. IoT Engineer.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

Altexsoft

DECEMBER 17, 2019

In the business sphere, a certain area of technology aims at helping people make the right decisions, by supporting them with the right data. This field is called business intelligence or BI. What is SAP Business Intelligence? Database and data management solutions. SAP Business Intelligence.

Business Intelligence 19

Business Intelligence Weak Development Team Systems Review Software Review 19

Altexsoft

AUGUST 21, 2019

Using specific tools and practices, businesses implement these methods to generate valuable insights. One of the most common ways how enterprises leverage data is business intelligence (BI), a set of practices and technologies that allow for transforming raw data into actionable information. Data scientists.

Development 44

Development Software Engineering Data Engineering Architecture 44