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. It aims at making data assets understandable and discoverable for users.

Tools 59

Tools Technical Review Software Review Systems Review 59

Altexsoft

JUNE 25, 2019

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Cloudera

SEPTEMBER 10, 2021

Shared Data Experience ( SDX ) on Cloudera Data Platform ( CDP ) enables centralized data access control and audit for workloads in the Enterprise Data Cloud. The public cloud (CDP-PC) editions default to using cloud storage (S3 for AWS, ADLS-gen2 for Azure).

Storage 79

Storage Cloud Azure Pharmaceuticals 79

Altexsoft

JULY 19, 2022

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. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two. What data mesh IS.

Architecture 52

Architecture Data Analytics Data Engineering 52

Cloudera

APRIL 26, 2023

Can you achieve similar outcomes with your on-premises data platform? Application modernization initiatives have led to cloud native architectures gaining popularity on premises, making it a sensible choice to extend to your data platform. This is exactly where cloud native architectures excel, and why they are so popular.

Cloud 63

Cloud Data Load Balancer Storage 63

Altexsoft

JANUARY 22, 2020

As a result, it became possible to provide real-time analytics by processing streamed data. 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. Stream processing.

Analytics 20

Analytics Data IoT Analysis 20

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. Data warehouse architecture.

Backup 115

Backup Azure Software Review Systems Review 115

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

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? Data pipeline components. Data pipeline components.

Data 76

Data Storage Analytics Data Center 76

O'Reilly Media - Ideas

JANUARY 25, 2024

While we like to talk about how fast technology moves, internet time, and all that, in reality the last major new idea in software architecture was microservices, which dates to roughly 2015. Who wants to learn about design patterns or software architecture when some AI application may eventually do your high-level design?

Trends 116

Trends Technical Review Technology Artificial Inteligence 116

Cloudera

FEBRUARY 22, 2022

Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists.

Data 103

Data Analytics Disaster Recovery Travel 103

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. Apache Hadoop architecture. What is Hadoop?

Big Data 59

Big Data Data Google Cloud Storage 59

Altexsoft

APRIL 26, 2022

We’ll dive deeper into Snowflake’s pros and cons, its unique architecture, and its features to help you decide whether this data warehouse is the right choice for your company. Data warehousing in a nutshell. 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

APRIL 16, 2021

An overview of data warehouse types. Optionally, you may study some basic terminology on data engineering or watch our short video on the topic: What is data engineering. What is data pipeline. This could be a transactional database or any other storage we take data from.

Analytics 98

Analytics Analysis Storage Business Intelligence 98

Altexsoft

JUNE 6, 2019

The GPU-based model consists of an ensemble (group) of multiple machine learning algorithms. The model forecasts demand for every product category using about 350 data features (problem properties). Building a data-driven organization. And it would be a pity to lose a customer because a product was out of stock.

Artificial Inteligence 89

Artificial Inteligence Artificial Intelligence Retail Innovation 89

Altexsoft

JUNE 26, 2023

Both data integration and ingestion require building data pipelines — series of automated operations to move data from one system to another. For this task, you need a dedicated specialist — a data engineer or ETL developer. Data engineering explained in 14 minutes. Find sources of relevant data.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. The number of possible applications tends to grow due to the rise of IoT , Big Data analytics , streaming media, smart manufacturing, predictive maintenance , and other data-intensive technologies.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Cloudera

APRIL 9, 2021

However, legacy data warehouses have limited support for public and private cloud deployment architectures. Managing disparate solutions is operationally inefficient, provides an inconsistent user experience and often leads to increased risk due to a lack of common security, governance and lineage of data. . Efficiency.

Data 58

Data Software Review Technical Review Architecture 58

Cloudera

AUGUST 20, 2021

The Cloudera Data Platform comprises a number of ‘data experiences’ each delivering a distinct analytical capability using one or more purposely-built Apache open source projects such as Apache Spark for Data Engineering and Apache HBase for Operational Database workloads.

Strategy 94

Strategy Data Technical Review Weak Development Team 94

Cloudera

NOVEMBER 28, 2017

To help support additional users and workloads, different data silos have proliferated throughout the business. The game-changing architecture of Altus Analytic DB eliminates the complexities of managing across all these different tenants and ensures simple, consistent security and governance.

Analytics 40

Analytics Cloud Data Data Engineering 40

Altexsoft

JUNE 7, 2021

Apache Hadoop is an open-source framework written in Java for distributed storage and processing of huge datasets. The keyword here is distributed since the data quantities in question are too large to be accommodated and analyzed by a single computer. Virtually, Hadoop puts no limits on the storage capacity. What is Hadoop.

Big Data 98

Big Data Tools Data Storage 98

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. The main technology drivers enabling cloud BI are: The ability to cost-effectively scale data storage in a single repository using cloud storage options such as Amazon S3 or Azure Data Lake Store (ADLS). Architecture patterns for the cloud.

Cloud 42

Cloud Data Storage Analytics 42

Altexsoft

NOVEMBER 9, 2021

Digitization has already greatly improved this business aspect, facilitating contract creation and editing, introducing e-signatures, increasing security, tracking expiration dates, and enabling convenient search and storage functionalities. This group of KPIs is focused on evaluating how efficient your procurement process is.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

AUGUST 4, 2021

You’ll also find out about the key types of data marts, their structure schemas, implementation steps, and more. What is a data mart? A data mart is a smaller subsection of a data warehouse built specifically for a particular subject area, business function, or group of users. Data mart constructing.

Data 64

Data Analytics Construction Cloud 64

Daniel Bryant

SEPTEMBER 13, 2019

What was worth noting was that (anecdotally) even engineers from large organisations were not looking for full workload portability (i.e. There were also two patterns of adoption of HashiCorp tooling I observed from engineers that I chatted to: Infrastructure-driven?—?in

Infrastructure 8

Infrastructure Azure Software Engineering Cloud 8

Altexsoft

MARCH 2, 2023

Essentially, the fundamental principle underlying this process is to recognize data as a valuable resource, given its significant role in driving business success. Data management is a technical implementation of data governance and involves the practical aspects of working with data, such as data storage, retrieval, and analysis.

Government 52

Government Tools Data Weak Development Team 52

Altexsoft

NOVEMBER 28, 2019

Let’s break them down: A data source layer is where the raw data is stored. Those are any of your databases, cloud-storages, and separate files filled with unstructured data. These are both a unified storage for all the corporate data and tools performing Extraction, Transformation, and Loading (ETL).

Business Intelligence 22

Business Intelligence Technical Review Development Storage 22

Altexsoft

OCTOBER 3, 2019

In March 2019, a provider of financial protection benefits Unum Group launched a closed pilot project with illness forecast application provider Sickweather. A provider maintains the platform and handles the storage of your data. Data engineer builds interfaces and infrastructure to enable access to data.

Analytics 16

Analytics Company Off-The-Shelf How To 16

Kentik

NOVEMBER 16, 2015

KDE handles over 10B flow records/day with a microservice architecture that's optimized using metrics. Here at Kentik, our Kentik Detect service is powered by a multi-tenant big data datastore called Kentik Data Engine. So it was critical to instrument every component leading to, around, and within our data engine.

Metrics 40

Metrics Microservices Linux Architecture 40

Kentik

JUNE 4, 2019

For the ISP’s executive and engineering teams, these variations increase the complexity of meeting two main operational goals: Ensuring the high throughput required for a good user experience. To succeed at these goals, an ISP needs to define a robust, performant architecture and also a set of best practices. How True Origin Works.

Video 56

Video Network Internet Groups 56

Confluent

OCTOBER 16, 2019

Using this data, Apache Kafka ® and Confluent Platform can provide the foundations for both event-driven applications as well as an analytical platform. With tools like KSQL and Kafka Connect, the concept of streaming ETL is made accessible to a much wider audience of developers and data engineers.

Data 19

Data Training Analytics Storage 19

Altexsoft

DECEMBER 9, 2020

Sometimes, a data or business analyst is employed to interpret available data, or a part-time data engineer is involved to manage the data architecture and customize the purchased software. At this stage, data is siloed, not accessible for most employees, and decisions are mostly not data-driven.

Analytics 102

Analytics Technical Review Technology Applications 102

Altexsoft

SEPTEMBER 20, 2019

Analyzing business information to facilitate data-driven decision making is what we call business intelligence or BI. In plain language, BI is a set of tools and methods to extract raw data from its source, transform it, load into a unified storage, and present to the user. Architecture of your database/data warehouse.

Business Intelligence 18

Business Intelligence Tools Data Analytics 18

Cloudera

FEBRUARY 14, 2022

When we introduced Cloudera Data Engineering (CDE) in the Public Cloud in 2020 it was a culmination of many years of working alongside companies as they deployed Apache Spark based ETL workloads at scale. It’s no longer driven by data volumes, but containerization, separation of storage and compute, and democratization of analytics.

Data Engineering 109

Data Engineering Engineering Data Storage 109

Altexsoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Weak Development Team 64

Weak Development Team Machine Learning Artificial Inteligence Software Review 64

Hu's Place - HitachiVantara

JANUARY 15, 2019

For lack of similar capabilities, some of our competitors began implying that we would no longer be focused on the innovative data infrastructure, storage and compute solutions that were the hallmark of Hitachi Data Systems. A midrange user now has access to the same, super-powerful features as the biggest banks.

Systems Review 72

Systems Review Storage System Software Review 72

Altexsoft

MARCH 14, 2023

Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. What is a modern data stack? The term “stack” in computing means a group of technologies working together to achieve a common goal. Modern data stack architecture.

Data 59

Data Technical Review Software Review Artificial Inteligence 59