Altexsoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

DECEMBER 10, 2020

Appealing directly to end-users in the Lines of Business (LOBs), these solutions can dramatically shorten time to value, lower administrative burdens, and promise continuous agility in response to changing business demands. 150 for storage use = $15 / TB / month x 10 TB. 150 for storage use = $15 / TB / month x 10 TB.

Data 98

Data Technical Review Storage Systems Review 98

Mentormate

OCTOBER 31, 2023

Consider setting deadlines for data collection, model training, and evaluation. Adopting an agile approach helps your team remain flexible and responsive, which is especially crucial in AI projects where initial assumptions change quickly. But don’t just define what you want to achieve. You must also map out how you’ll get there.

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

Cloudera

JANUARY 15, 2021

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. Multiple analytical engines within the broader CDP (data warehousing, machine learning, data engineering) can operate on the same shared data in these file formats.

Performance 90

Performance Cloud Data Storage 90

Cloudera

DECEMBER 20, 2022

Today, the costs of sensors, data capture, and information storage have significantly decreased and are one tenth of what they were 10 years ago, leading to the proliferation of data that enables advanced analytics-driving efficiencies. The demand for energy in the retail market has been practically flat in recent years.

Energy 79

Energy Analytics Data Machine Learning 79

O'Reilly Media - Ideas

APRIL 9, 2019

A few months ago, I wrote about the differences between data engineers and data scientists. An interesting thing happened: the data scientists started pushing back, arguing that they are, in fact, as skilled as data engineers at data engineering. Data engineering is not in the limelight.

Data Engineering 111

Data Engineering Engineering Data Technical Review 111

Altexsoft

AUGUST 25, 2020

That’s why a data specialist with big data skills is one of the most sought-after IT candidates. Data Engineering positions have grown by half and they typically require big data skills. Data engineering vs big data engineering. This greatly increases data processing capabilities.

Big Data 75

Big Data Data Engineering Engineering Data 75

RapidValue

JULY 21, 2019

DataOps aids data practitioners to continuously deliver quality data to applications and business processes. The end-users of data, like the data analysts and data scientists, work closely with both data engineers and IT Ops in order to deliver continuous data movement.

Business Transformation 65

Business Transformation Enterprise DevOps Analytics 65

Hu's Place - HitachiVantara

JANUARY 3, 2019

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. More focus will be on the operational aspects of data rather than the fundamentals of capturing, storing and protecting data. Happy New Year and welcome to 2019, a year full of possibilities.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

TIBCO - Connected Intelligence

APRIL 22, 2021

In its annual Worldwide Global Datasphere Forecast, 2019-2023, IDC projected that only 15% of annual data growth is actually net new data. That means 85% of data growth results from copying data you already have. Business-friendly data views simplify access and hide IT complexity.

Sustainability 46

Sustainability Virtualization Data Energy 46

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

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. When you execute a query against the data lake, data transformation are performed at run-time or in-memory. Classic ETL.

Storage 40

Storage Big Data Google Cloud Analysis 40

d2iq

FEBRUARY 19, 2021

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

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

Cloudera

NOVEMBER 28, 2023

Cloudera Private Cloud Data Services is a comprehensive platform that empowers organizations to deliver trusted enterprise data at scale in order to deliver fast, actionable insights and trusted AI. This means you can expect simpler data management and drastically improved productivity for your business users.

Data 78

Data Scalability Analytics Quality Assurance 78

Cloudera

APRIL 26, 2023

At its core, CDP Private Cloud Data Services (“the platform”) is an end-to-end cloud native platform that provides a private open data lakehouse. It offers features such as data ingestion, storage, ETL, BI and analytics, observability, and AI model development and deployment.

Cloud 63

Cloud Data Load Balancer Storage 63

Altexsoft

FEBRUARY 10, 2021

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. It covers the entire data analytics lifecycle, from data extraction to visualization and reporting, using Agile practices to speed up business results.

Analytics 75

Analytics DevOps Development Software Review 75

O'Reilly Media - Ideas

MARCH 31, 2020

There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.” Shipping any machine learning system requires a huge mountain of organizational and data engineering effort, so the ultimate payoff needs to match that investment.

Product Management 136

Product Management Artificial Inteligence Machine Learning Software Review 136

Altexsoft

JULY 19, 2022

This basic principle corresponds to that of agile software development or approaches such as DevOps, Domain-Driven Design, and Microservices: DevOps (development and operations) is a practice that aims at merging development, quality assurance, and operations (deployment and integration) into a single, continuous set of processes.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

NOVEMBER 23, 2020

Otherwise, let’s start from the most basic question: What is data migration? What is data migration? In general terms, data migration is the transfer of the existing historical data to new storage, system, or file format. What makes companies migrate their data assets. Main types of data migration.

Data 104

Data Transportation Backup Storage 104

Cloudera

OCTOBER 25, 2022

The data lakehouse is gaining in popularity because it enables a single platform for all your enterprise data with the flexibility to run any analytic and machine learning (ML) use case. 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

O'Reilly Media - Ideas

JANUARY 25, 2024

Before that, cloud computing itself took off in roughly 2010 (AWS was founded in 2006); and Agile goes back to 2000 (the Agile Manifesto dates back to 2001, Extreme Programming to 1999). Data analysis and databases Data engineering was by far the most heavily used topic in this category; it showed a 3.6%

Trends 115

Trends Technical Review Technology Artificial Inteligence 115

Altexsoft

SEPTEMBER 10, 2021

It involves combining data from disparate sources, removing its redundancies, cleaning up any errors, and aggregating it within a single data store like a data warehouse. The delivery style to consolidate data is common data storage we’re covering below. How data consolidation works.

Tools 52

Tools Data Software Review Technical Review 52

Cloudera

NOVEMBER 28, 2017

However, different departments or user groups may have access to different subsets of data, making it difficult to join and analyze data between them and limiting collaboration between different teams (such as for workflows requiring data engineers, data scientists, and SQL users).

Analytics 40

Analytics Cloud Data Data Engineering 40

Cloudera

SEPTEMBER 9, 2018

Businesses need to be agile and quick in using data to drive insights, lest they lose the opportunity they are trying to capitalize on. It’s no surprise that there is a strong correlation between successful businesses and those that exceed in extracting value from data. Using Cloudera Altus for your cloud data warehouse.

Data 40

Data Data Engineering Analytics Cloud 40

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. Organizations find they have much more agility with analytics in the cloud and can operate at a lower cost point than has been possible with legacy on-premises solutions. Published originally on O’Reilly.com. Clouds (source: Pexels ).

Cloud 42

Cloud Data Storage Analytics 42

TIBCO - Connected Intelligence

DECEMBER 14, 2020

On-premises, cloud, hybrid, and multi-cloud are just some of the locations where your data is now stored. Each provides value in an increasing data driven business climate. Today’s best compute and storage options will be surpassed tomorrow. On the consumption side, your demand for data has never been higher.

Virtualization 26

Virtualization Data Architecture Agile 26

Cloudera

DECEMBER 7, 2017

This means cloud makes it too easy to accidentally create a lot of redundant silos of data for each of those cloud analytics services. That data may be stored multiple times in different pools, each multiplying storage resource costs. That data may be hard to discover for other users and other applications.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

Kentik

JUNE 13, 2016

Kentik Detect collects network telemetry at service provider speed and scale, ingesting and storing terabits of full-resolution network data including NetFlow, Geo-IP, BGP, SNMP, and network performance data. The data is not merely correlated but fully unified to create useful metadata for enhanced context.

Network 40

Network LAN Big Data Scalability 40

Cloudera

SEPTEMBER 10, 2018

Multidisciplinary analytics are the tools, the different workloads you need: data engineering, data warehousing, data science, and operational analytics. Therefore, it’s crucial that we provide complete consistency across the entire realm of data context: security, governance, lifecycle, control and data catalog.

Analytics 40

Analytics Machine Learning Artificial Inteligence Cloud 40

Altexsoft

AUGUST 4, 2021

For example, a company has a data mart containing all the financial data. The company may wish to model an OLAP cube to summarize this data by different dimensions: by time, by product, or by city, to name a few. Watch our video about data engineering to learn more about how data gets from sources to BI tools.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

MARCH 2, 2023

There’s a centralized structure that provides a framework, which is then used by autonomous departments that own their data and metadata. Benefits include more autonomy, faster issue resolution, and improved agility. Its goal is to define and control all data governance initiatives. sales specialists).

Government 52

Government Tools Data Weak Development Team 52

Cloudera

DECEMBER 12, 2018

For example, Impala uses the Hive metastore catalog as its data dictionary and it operates directly on data existing in HDFS, which is found through the Namenode API. So, they have the same components: a catalog, query compilation, query execution, and file management and storage, yet they’re independent of each other.

Data 41

Data Marketing Storage Big Data 41

Tenable

NOVEMBER 3, 2022

When our data engineering team was enlisted to work on Tenable One, we knew we needed a strong partner. When Tenable’s product engineering team came to us in data engineering asking how we could build a data platform to power the product, we knew we had an incredible opportunity to modernize our data stack.

Data 98

Data AWS Storage Data Engineering 98

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

Cloudera

JANUARY 11, 2021

Cloudera Data Warehouse (CDW) is a cloud service and an integral part of the newly released Cloudera Data Platform (CDP). Key features are: Highly scalable and performant open-source engines for BI and data warehousing workloads. Separate compute from storage. Data can be served to the business with ease.

Data 61

Data Pharmaceuticals Machine Learning Artificial Inteligence 61

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