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. More formats, more engines, more interoperability.

Data 94

Data Analytics Open Source Architecture 94

Altexsoft

OCTOBER 30, 2021

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

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

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. 150 for storage use = $15 / TB / month x 10 TB.

Data 98

Data Technical Review Storage Systems Review 98

Altexsoft

APRIL 22, 2020

So, along with data scientists who create algorithms, there are data engineers, the architects of data platforms. In this article we’ll explain what a data engineer is, the field of their responsibilities, skill sets, and general role description. What is a data engineer?

Data Engineering 69

Data Engineering Engineering Artificial Inteligence Data 69

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 84

Data Analytics Cloud Technical Review 84

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

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. Metadata storage usually implies developing a specialized repository. Metadata storage. What is metadata management?

Tools 59

Tools Technical Review Software Review Systems Review 59

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. Data siloes.

Technical Review 59

Technical Review Software Review Analytics 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

Altexsoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Datavail

JUNE 15, 2023

“It unifies the business model across all the different types of analytics workloads, whether they’re SQL, machine learning – whatever job you want, you can use the same compute infrastructure. Data Factory : A data integration tool with 150+ connectors to cloud and on-premises data sources.

Azure 52

Azure Analytics Storage Generative AI 52

Cloudera

JUNE 30, 2022

Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP) — including Cloudera Data Warehousing ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). Read why the future of data lakehouses is open.

Data 89

Data Analytics Machine Learning Artificial Inteligence 89

Altexsoft

MARCH 31, 2020

It means you must collect transactional data and move it from the database that supports transactions to another system that can handle large volumes of data. And, as is common, to transform it before loading to another storage system. But how do you move data? The simplest illustration for a data pipeline.

Data 76

Data Storage Analytics Data Center 76

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

Cloudera

AUGUST 30, 2022

Agencies are plagued by a wide range of data formats and storage environments—legacy systems, databases, on-premises applications, citizen access portals, innumerable sensors and devices, and more—that all contribute to a siloed ecosystem and the data management challenge. . That’s just the tip of the iceberg.

Architecture 80

Architecture Data Artificial Inteligence Artificial Intelligence 80

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

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

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. What is ELT?

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Altexsoft

AUGUST 21, 2019

Dedicated fields of knowledge like data engineering and data science became the gold miners bringing new methods to collect, process, and store data. Using specific tools and practices, businesses implement these methods to generate valuable insights. But it can’t be used in its raw format. Who is ETL Developer?

Development 44

Development Software Engineering Data Engineering Architecture 44

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

Altexsoft

JULY 29, 2022

Apache Hadoop is an open-source Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. 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. What is Hadoop? Apache Hadoop architecture.

Big Data 59

Big Data Data Google Cloud Storage 59

Cloudera

OCTOBER 25, 2022

Enabling data and analytics in the cloud allows you to have infinite scale and unlimited possibilities to gain faster insights and make better decisions with data. Cloud data lakehouses provide significant scaling, agility, and cost advantages compared to cloud data lakes and cloud data warehouses.

Cloud 52

Cloud Data Firewall AWS 52

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

Cloudera

DECEMBER 7, 2017

Cloud promises many advantages as an environment for machine learning and analytics. Cloud offers elasticity of those resources to efficiently support transient analytics workloads and data pipelines. That data may be stored multiple times in different pools, each multiplying storage resource costs.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

Cloudera

NOVEMBER 2, 2020

It has the key elements of fast ingest, fast storage, and immediate querying for BI purposes. Basic Architecture for Real-Time Data Warehousing. These include stream processing/analytics, batch processing, tiered storage (i.e. for active archive or joining live data with historical data), or machine learning.

Data 97

Data Analytics Storage Big Data 97

Altexsoft

FEBRUARY 22, 2022

Such an approach requires a great deal of investment since a whole ecosystem has to be created, including IoT sensors installation, acquiring specialized software, creating and maintaining machine learning (ML) models, engaging IT and data science specialists , and so on. Processing data.

Software Review 52

Software Review Technical Review Software Technology 52

Cloudera

SEPTEMBER 9, 2018

It enables organizations to address their data warehousing reporting needs by making it quick and easy to consolidate data into a single repository yet still service your users’ needs in a scalable and cost-effective way. Using Cloudera Altus for your cloud data warehouse. Try Altus Data Warehouse today.

Data 40

Data Data Engineering Analytics Cloud 40

Altexsoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. Data storage and processing. billion data points.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

NOVEMBER 9, 2021

Traditional statistical methods use mainly internal, historical data to predict trends within relatively stable markets. Meanwhile, machine learning (ML) techniques are capable of processing a wide range of both historical and current data from multiple external and internal sources. Extract data. Consolidate data.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

AUGUST 4, 2021

Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit. What is a data mart? Data mart use cases. Time-limited data projects.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

OCTOBER 24, 2019

Similar to humans companies generate and collect tons of data about the past. And this data can be used to support decision making. While our brain is both the processor and the storage, companies need multiple tools to work with data. And one of the most important ones is a data warehouse. Subject-oriented data.

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

OCTOBER 3, 2019

Predictive analytics requires numerous statistical techniques, including data mining (detecting patterns in data) and machine learning. Organizations already use predictive analytics to optimize operations and learn how to improve the employee experience. Let’s explore several popular areas of its application.

Analytics 16

Analytics Company Off-The-Shelf How To 16

Altexsoft

DECEMBER 14, 2022

The heart and soul of Docker are containers — lightweight virtual software packages that combine application source code with all the dependencies such as system libraries (libs) and binary files as well as external packages, frameworks, machine learning models, and more. The Good and the Bad of the SAP Business Intelligence Platform.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

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

Data 40

Data Scalability Analytics Sport 40

Altexsoft

NOVEMBER 22, 2019

There are several pillar data sets you have to consider in the first place. Important hotel data sets and overlaps between them. Booking and property data. The main storage of hotel booking information is your property management system (PMS). Data processing in a nutshell and ETL steps outline. Source: DJUBO.

Hotels 18

Hotels Data Technical Review Systems Review 18