CIO

MAY 21, 2024

In the past, to get at the data, engineers had to plug a USB stick into the car after a race, download the data, and upload it to Dropbox where the core engineering team could then access and analyze it. If I don’t do predictive maintenance, if I have to do corrective maintenance at events, a lot of money is wasted.”

Data 190

Data Azure Engineering Analytics 190

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. Big data processing. maintaining data pipeline.

Big Data 75

Big Data Data Engineering Engineering Data 75

AWS Machine Learning - AI

MARCH 13, 2025

In this post, we dive deeper into one of MaestroQAs key featuresconversation analytics, which helps support teams uncover customer concerns, address points of friction, adapt support workflows, and identify areas for coaching through the use of Amazon Bedrock.

Generative AI 113

Generative AI CTO Coach AWS Artificial Inteligence 113

Altexsoft

FEBRUARY 8, 2021

These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development. Optimization opportunities offered by analytics.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

CIO

APRIL 27, 2023

For technologists with the right skills and expertise, the demand for talent remains and businesses continue to invest in technical skills such as data analytics, security, and cloud. The demand for specialized skills has boosted salaries in cybersecurity, data, engineering, development, and program management.

Technical Review 174

Technical Review Software Review Systems Review Software Engineering 174

O'Reilly Media - Ideas

AUGUST 18, 2020

We’ll share why in a moment, but first, we want to look at a historical perspective with what happened to data warehouses and data engineering platforms. Lessons Learned from Data Warehouse and Data Engineering Platforms. This is an open question, but we’re putting our money on best-of-breed products.

Machine Learning 145

Machine Learning Artificial Inteligence Data Data Engineering 145

Altexsoft

MAY 14, 2021

And that’s the most important thing: Big Data analytics helps companies deal with business problems that couldn’t be solved with the help of traditional approaches and tools. This post will draw a full picture of what Big Data analytics is and how it works. Big Data and its main characteristics.

Big Data 78

Big Data Analytics Tools Applications 78

O'Reilly Media - Ideas

FEBRUARY 11, 2020

Key survey results: The C-suite is engaged with data quality. Data scientists and analysts, data engineers, and the people who manage them comprise 40% of the audience; developers and their managers, about 22%. Data quality might get worse before it gets better. An additional 7% are data engineers.

Weak Development Team 145

Weak Development Team Data Technical Review Survey 145

Datavail

MARCH 31, 2022

Digital analytics offer enterprises an almost limitless array of values because they are as malleable as each business needs them to be. Further, these analytical capacities continue to evolve as more companies develop proprietary analytics to meet their specific sector demands. Analytics as a Strategy Tool.

Business Analytics 52

Business Analytics Analytics Artificial Inteligence Strategy 52

Cloudera

SEPTEMBER 29, 2020

CDW outperformed HDInsight by over 40% in total query runtime for TPC-DS queries using the same hardware specs (see Figure 1). CDW is an analytic offering for Cloudera Data Platform (CDP). In addition to better performance, CDW also provides a SaaS like experience to seamlessly manage your data lifecycle needs.

Azure 120

Azure Data Comparison Virtualization 120

Erik Bernhardsson

DECEMBER 6, 2022

Data teams often need to change infrastructure a lot more often (sometimes every new cron job needs a Terraform update), have very “bursty” needs for compute power, and needs a much wider range of hardware (GPUs! There's a weird sort of backend-normative view of what data teams should do, but I think it's very misguided.

CTO Coach 242

CTO Coach Fractional CTO Software Engineering Serverless 242

Cloudera

AUGUST 26, 2020

Informatica and Cloudera deliver a proven set of solutions for rapidly curating data into trusted information. Informatica’s comprehensive suite of Data Engineering solutions is designed to run natively on Cloudera Data Platform — taking full advantage of the scalable computing platform.

Data 84

Data Machine Learning Artificial Inteligence Disaster Recovery 84

Altexsoft

FEBRUARY 11, 2023

Data is now one of the most valuable assets for any kind of business. The 11th annual survey of Chief Data Officers (CDOs) and Chief Data and Analytics Officers reveals 82 percent of organizations are planning to increase their investments in data modernization in 2023. Feel free to enjoy it. Feel free to enjoy it.

Data 87

Data Data Engineering Big Data Architecture 87

Cloudera

MAY 3, 2021

data is generated – at the Edge. In the last five years, there has been a meaningful investment in both Edge hardware compute power and software analytical capabilities. Real-time and time series data is growing 50% faster than static data forms and streaming analytics is projected to grow at a 34% CAGR.

Scalability 103

Scalability Agile Data Systems Review 103

Cloudera

NOVEMBER 28, 2023

With its rise in popularity generative AI has emerged as a top CEO priority, and the importance of performant, seamless, and secure data management and analytics solutions to power those AI applications is essential. This means you can expect simpler data management and drastically improved productivity for your business users.

Data 84

Data Scalability Analytics Quality Assurance 84

Cloudera

APRIL 22, 2021

Today’s enterprise data analytics teams are constantly looking to get the best out of their platforms. Storage plays one of the most important roles in the data platforms strategy, it provides the basis for all compute engines and applications to be built on top of it. Standard Benchmarks.

Data 101

Data Storage Architecture Big Data 101

Altexsoft

MARCH 31, 2020

Only after these actions can you analyze data with dedicated software (a so-called online analytical processing or OLAP system). But how do you move data? You need to have infrastructure, hardware and/or software, that will allow you to do that. You need an efficient data pipeline. What is a data pipeline?

Data 76

Data Storage Analytics Data Center 76

Cloudera

NOVEMBER 2, 2020

Having a live view of all aspects of their network lets them identify potentially faulty hardware in real time so they can avoid impact to customer call/data service. Ingest 100s of TB of network event data per day . Updates and deletes to ensure data correctness. Optimized for point lookups, analytics, mutations, etc.

Data 97

Data Analytics Storage Big Data 97

Altexsoft

NOVEMBER 9, 2021

If you want to streamline your procurement and gain more visibility into this process, you have to get hold of available data, analyze it, and extract value to make informed decisions. What is procurement analytics and the opportunities it offers? Main components of procurement analytics. Procurement and its challenges.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

CIO

JUNE 19, 2024

Bring the right skills onboard As a baseline, every platform engineering team needs to hire people who have strong communication skills, are technically proficient in software development, hardware and data, have excellent analytical and problem solving skills, and are familiar with platform engineering tools, says Atkinson.

Weak Development Team 208

Weak Development Team Engineering UI/UX Software Development 208

Altexsoft

JANUARY 14, 2021

Non-volatile implies that once the data flies into a warehouse, it stays there and isn’t removed with new data enterings. As such, it is possible to retrieve old archived data if needed. Summarized touches upon the fact the data is used for data analytics. Data warehouse architecture.

Backup 115

Backup Azure Software Review Systems Review 115

CIO

JUNE 14, 2023

Going from petabytes (PB) to exabytes (EB) of data is no small feat, requiring significant investments in hardware, software, and human resources. This can be achieved by utilizing dense storage nodes and implementing fault tolerance and resiliency measures for managing such a large amount of data. Much larger.

Data 97

Data Scalability Storage Big Data 97

Cloudera

MAY 17, 2021

Modernizing your data warehousing experience with the cloud means moving from dedicated, on-premises hardware focused on traditional relational analytics on structured data to a modern platform. This will of course translate into more options to explore data, leading to faster, better and more business insights.

Cloud 90

Cloud Data Agile Analytics 90

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. These are data engineers who are responsible for implementing these processes. Data size and type. ELT comes to the rescue. What is ELT?

Systems Review 109

Systems Review Technical Review Software Review Big Data 109

Altexsoft

JUNE 7, 2021

The framework provides a way to divide a huge data collection into smaller chunks and shove them across interconnected computers or nodes that make up a Hadoop cluster. As a result, a Big Data analytics task is split up, with each machine performing its own little part in parallel. No real-time data processing.

Big Data 98

Big Data Tools Data Storage 98

Altexsoft

OCTOBER 24, 2019

Enterprise data warehouse vs usual data warehouse: what’s the difference? Any data warehouse is a database which is always connected with raw-data sources via data integration tools on one end and analytical interfaces on the other. Reflects the source data. Subject-oriented data.

Architecture 50

Architecture Enterprise Data Technical Review 50

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. 2019 will provide even more proof points.

Systems Review 72

Systems Review Storage System Software Review 72

O'Reilly Media - Ideas

MARCH 18, 2020

The sample is far from tech-laden, however: the only other explicit technology category—“Computers, Electronics, & Hardware”—accounts for less than 7% of the sample. Data scientists dominate, but executives are amply represented. One-sixth of respondents identify as data scientists, but executives—i.e.,

Enterprise 140

Enterprise Survey Technical Review Weak Development Team 140

UruIT

JULY 23, 2019

Microsoft’s set of tools for machine learning includes Azure Machine Learning (which also covers Azure Machine Learning Studio), Power BI, Azure Data Lake, Azure HDInsight, Azure Stream Analytics and Azure Data Factory. Nowadays, MathWorks provides users with a sustained visibility in the general advanced analytics field.

Artificial Inteligence 81

Artificial Inteligence Machine Learning Azure Software Review 81

O'Reilly Media - Ideas

NOVEMBER 13, 2018

The main bottleneck here is speed: many researchers are actively investigating hardware and software tools that can speed up model inference (and perhaps even model building) on encrypted data. One important change outlined in the report is the need for a set of data scientists who are independent from this model-building team.

Machine Learning 111

Machine Learning Artificial Inteligence Software Review Conference 111

UruIT

APRIL 27, 2020

Offers building blocks for creating a solution to a data science problem; . Grants support for carrying out data and analytics tasks; . Allows data scientists and developers to take on tasks that encompass visualization, interactive exploration, deployment, performance engineering, data preparation, and data access. .

Artificial Inteligence 78

Artificial Inteligence Machine Learning Azure Software Review 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 64

Cloud Data Load Balancer Storage 64

O'Reilly Media - Ideas

MARCH 1, 2022

ApacheHop is a metadata-driven data orchestration for building dataflows and data pipelines. It integrates with Spark and other data engines, and is programmed using a visual drag-and-drop interface, so it’s low code. That’s a distinct possibility, and a nightmare for security professionals. No blockchain required.

Trends 117

Trends Blockchain Serverless Malware 117

Altexsoft

OCTOBER 2, 2020

A data architect focuses on building a robust infrastructure so that data brings business value. Data modeling: creating useful and meaningful data entities. Data integration and interoperability: consolidating data into a single view. There are two main approaches to data integration.

Strategy 64

Strategy Database Administration Data Technical Review 64

Erik Bernhardsson

OCTOBER 27, 2015

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences? So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering. How do you respond when you hear the phrase ‘big data’? Write a ton of code. Don’t watch TV :).

Data 101

Data Big Data Machine Learning Artificial Inteligence 101

Erik Bernhardsson

OCTOBER 27, 2015

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences? So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering. How do you respond when you hear the phrase ‘big data’? Write a ton of code. Don’t watch TV :).

Data 100

Data Big Data Machine Learning Artificial Inteligence 100

ParkMyCloud

MAY 29, 2019

A BI analyst has strong skills in database technology, analytics, and reporting tools and excellent knowledge and understanding of computer science, information systems or engineering. BI Analyst can also be described as BI Developers, BI Managers, and Big Data Engineer or Data Scientist.

Trends 72

Trends Cloud IoT Artificial Inteligence 72