InnovationM

MAY 14, 2023

Introduction As someone who has hands-on experience in constructing and leveraging data lakes, I can attest to the transformative power these repositories hold for organizations grappling with vast amounts of data. Metadata management tools ensure a clear understanding of the data landscape and enhance data stewardship practices.

Data 52

Data Storage Construction Business Intelligence 52

Cloudera

MARCH 2, 2021

However, in the typical enterprise, only a small team has the core skills needed to gain access and create value from streams of data. This data engineering skillset typically consists of Java or Scala programming skills mated with deep DevOps acumen. A rare breed. What do you mean by democratizing?

Data 112

Data Weak Development Team Data Engineering Scalability 112

Altexsoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data 87

Data Data Engineering Big Data Architecture 87

KitelyTech

FEBRUARY 3, 2021

Big data and data science are important parts of a business opportunity. Developing business intelligence gives them a distinct advantage in any industry. How companies handle big data and data science is changing so they are beginning to rely on the services of specialized companies.

Big Data 52

Big Data Data Development Business Intelligence 52

Cloudera

AUGUST 30, 2022

Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Deploying modern data architectures.

Architecture 78

Architecture Data Artificial Inteligence Artificial Intelligence 78

Altexsoft

MARCH 30, 2023

The answer is simple: They use the same technology to make the most of data. Along with thousands of other data-driven organizations from different industries, the above-mentioned leaders opted for Databrick to guide strategic business decisions. How data engineering works in 14 minutes. Source: Databricks.

Weak Development Team 64

Weak Development Team Machine Learning Artificial Inteligence Software Review 64

Altexsoft

MARCH 18, 2021

It is the process of collecting raw data from disparate sources, transmitting it to a staging database for conversion, and loading prepared data into a unified destination system. As data keeps growing in volumes and types, the use of ETL becomes quite ineffective, costly, and time-consuming. ELT comes to the rescue.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Altexsoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. Scalability. ELT vs ETL.

Tools 52

Tools Software Review Systems Review Testing 52

Cloudera

NOVEMBER 19, 2020

Furthermore, the same tools that empower cybercrime can drive fraudulent use of public-sector data as well as fraudulent access to government systems. Technology can help. Too often, though, legacy systems cannot deliver the needed speed and scalability to make these analytic defenses usable across disparate sources and systems.

Government 117

Government Artificial Inteligence Machine Learning Data 117

Altexsoft

JANUARY 14, 2021

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. For over 30 years, data warehouses have been a rich business-insights source. Scalability opportunities.

Backup 115

Backup Azure Software Review Systems Review 115

Datavail

SEPTEMBER 8, 2022

According to an IDG survey , companies now use an average of more than 400 different data sources for their business intelligence and analytics processes. What’s more, 20 percent of these companies are using 1,000 or more sources, far too many to be properly managed by human data engineers.

Data 40

Data Nonprofit Quality Assurance Education 40

Hu's Place - HitachiVantara

JUNE 5, 2019

.” In the last half of last year we applied DataOps to consolidate our enterprise reporting systems into one data lake. 32 data domain owners from governance, pricing, services, procurement, partners, sales, marketing, supply planning and IT were involved. Business owners reported immediate benefits from this implementation.

Technical Review 40

Technical Review Analytics Data Engineering Scalability 40

Agile Engine

DECEMBER 27, 2023

A data analytics consultancy has a team of specialists and engineers who perform data analytics for companies that don’t have the capacity to do it in-house. Adaptability and scalability that come with consultancies being able to scale resources up or down as needed.

Big Data 52

Big Data Analytics Data Analysis 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. Data warehousing in a nutshell. Modern data pipeline with Snowflake technology as its part.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

OCTOBER 2, 2020

Key disciplines and roles in data management. Data architecture: aligning technologies with business goals. Specialist responsible for the area: data architect. Data architecture is a starting point for any data management model. Data analytics and business intelligence: drawing insights from data.

Database Administration 64

Database Administration Strategy Data Technical Review 64

Altexsoft

OCTOBER 21, 2022

Kafka can continue the list of brand names that became generic terms for the entire type of technology. Similar to Google in web browsing and Photoshop in image processing, it became a gold standard in data streaming, preferred by 70 percent of Fortune 500 companies. Scalability. Scalability is one of Kafka’s key selling points.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

MARCH 14, 2023

In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support. This development has paved the way for a suite of cloud-native data tools that are user-friendly, scalable, and affordable. Data democratization.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

Altexsoft

JULY 19, 2022

No matter how ridiculous it may sound, the famous quote is applicable to the technology world in many ways. In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. What data mesh is and is not.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

MAY 14, 2021

The concept of Big Data isn’t new: It has been the desired fruit for several decades already as the capabilities of software and hardware have made it possible for companies to successfully manage vast amounts of complex data. The process of identifying the sources and then getting Big Data varies from company to company.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

JULY 29, 2022

The biggest star of the Big Data world, Hadoop was named after a yellow stuffed elephant that belonged to the 2-year son of computer scientist Doug Cutting. The toy became the official logo of the technology, used by the major Internet players — such as Twitter, LinkedIn, eBay, and Amazon. Scalability. The Hadoop toy.

Big Data 59

Big Data Data Google Cloud Storage 59

StubHub

SEPTEMBER 20, 2018

Analysts focus on ad-hoc, in-depth analytics to provide insightful business intelligence and product analysis. Infinite capacity and scalability The data system?—?including including data storage, pipeline, analytic platform and machine learning platform?—?is is cloud-based and scalable based on needs and usage.

Data 40

Data Scalability Analytics Sport 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. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. How data engineering works in a nutshell.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Cloudera

NOVEMBER 2, 2020

Users today are asking ever more from their data warehouse. This is resulting in advancements of what is provided by the technology, and a resulting shift in the art of the possible. But an important caveat is that ingest speed, semantic richness for developers, data freshness, and query latency are paramount. Data Hub –

Data 94

Data Analytics Storage Big Data 94

Altexsoft

SEPTEMBER 20, 2019

Not only technological companies are concerned about data analysis, but any kind of business is. Analyzing business information to facilitate data-driven decision making is what we call business intelligence or BI. How is data visualized in BI? Data visualization tools and libraries.

Business Intelligence 18

Business Intelligence Tools Data Analytics 18

Altexsoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. For this task, you need a dedicated specialist — a data engineer or ETL developer.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Cloudera

JULY 9, 2018

It is cliché to discuss IT/business solutions as people, process, and technology. Since the 1960s, technology has disrupted business through the advent of computing and information management. Companies that didn’t adopt technology were soon out of business or in decline. The New Iron Triangle. The Solution.

Machine Learning 42

Machine Learning Artificial Inteligence Data Analytics 42

Altexsoft

APRIL 13, 2023

Source: Supply Chain Dive Over the last few years, global supply chains have been so severely disrupted – but also enhanced with cutting-edge technologies. Leading executives focus on building resilient and intelligent supply chains that can withstand the turmoil due to data-based proactive decisions.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Altexsoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Let’s elaborate on this and figure out how a data lakehouse is different from its ancestors and name inspirers in more detail. Data warehouse.

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. Business intelligence (BI) and analytics in the cloud is an area that has gained the attention of many organizations looking to provide a better user experience for their data analysts and engineers. Clouds (source: Pexels ).

Cloud 43

Cloud Data Storage Analytics 43

Altexsoft

OCTOBER 8, 2021

As a rule, good data integration products have. easy-to-use interfaces; capabilities to examine, clean, and transform data; native connectors for different data integration use cases; scalability and elasticity to fit the changing landscape of data; and. Data profiling and cleansing. high security.

Tools 52

Tools Data Software Review Open Source 52

Altexsoft

DECEMBER 14, 2022

Launched in 2013 as an open-source project, the Docker technology made use of existing computing concepts around containers, specifically the Linux kernel with its features. As we provided you with the overview of Docker technology, it’s time to look under the hood of its ecosystem. Scalability. scaling application environments.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

Cloudera

DECEMBER 10, 2020

Some data warehousing solutions such as appliances and engineered systems have attempted to overcome these problems, but with limited success. . Recently, cloud-native data warehouses changed the data warehousing and business intelligence landscape. Watch this video to get an overview of CDW. .

Data 98

Data Technical Review Storage Systems Review 98

Altexsoft

OCTOBER 24, 2019

With a data warehouse, an enterprise is able to manage huge data sets, without administering multiple databases. Such practice is a futureproof way of storing data for business intelligence (BI) , which is a set of methods/technologies of transforming raw data into actionable insights.

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

SEPTEMBER 9, 2022

Metadata management is a set of activities, technologies, and policies that target metadata collection, storage, and organizing. It aims at making data assets understandable and discoverable for users. Data quality KPIs monitoring helps ensure data quality by tracking essential metrics. What is metadata management?

Tools 59

Tools Technical Review Software Review Systems Review 59

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