CIO

JUNE 22, 2023

There’s a high demand for software engineers, data engineers, business analysts and data scientists, as finance companies move to build in-house tools and services for customers. Other relevant roles include security professionals, project managers, UX/UI designers, product managers, data analysts, and business analysts.

Industry 306

Industry UI/UX Telecommunications Quality Assurance 306

CIO

FEBRUARY 14, 2023

Investments in artificial intelligence are helping businesses to reduce costs, better serve customers, and gain competitive advantage in rapidly evolving markets. Organizations have balanced competing needs to make more efficient data-driven decisions and to build the technical infrastructure to support that goal.

Artificial Inteligence 290

Artificial Inteligence Strategy Data Machine Learning 290

Mobilunity

MAY 8, 2023

Key zones of an Enterprise Data Lake Architecture typically include ingestion zone, storage zone, processing zone, analytics zone, and governance zone. Ingestion zone is where data is collected from various sources and ingested into the data lake. Storage zone is where the raw data is stored in its original format.

Engineering 52

Engineering Data Storage AWS 52

Xebia

MAY 16, 2023

Data Engineers were tempted by the pressure of the moment to give up on testing all together. There was no need for generating your own data; just take a percentage of production data. In many cases, these tasks ended up on the shoulders of the Data Engineers themselves. Overly restrictive governance.

Testing 130

Testing How To Data Engineering Engineering 130

CIO

JULY 6, 2023

OCI’s Supercluster includes OCI Compute Bare Metal, which provides an ultralow-latency remote direct access memory (RDMA) over a Converged Ethernet (RoCE) cluster for low-latency networking, and a choice of high-performance computing storage options.

Generative AI 283

Generative AI Artificial Inteligence Strategy Google Cloud 283

Altexsoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. What is the main difference between a data architect and a data engineer? By the way, we have a video dedicated to the data engineering working principles.

Data 87

Data Data Engineering Big Data Architecture 87

CIO

JUNE 7, 2022

Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. Data analytics and data science are closely related.

Analytics 338

Analytics Data Analysis Business Analytics 338

Erik Bernhardsson

DECEMBER 15, 2020

There will be a temporary surplus of diapers on the market, and the market will converge to a new equilibrium where the supply shrinks to meet demand. The difference with software engineers is that demand grows when the cost goes down. You need storage to build something to serve 1M concurrent users? What's the result?

Software Engineering 304

Software Engineering Engineering Tools Software 304

Cloudera

DECEMBER 10, 2020

Why worry about costs with cloud-native data warehousing? Have you been burned by the unexpected costs of a cloud data warehouse? If so, you know about the failed economics of some cloud-native solutions on the market today. If not, before adopting a cloud data warehouse, consider the true costs of a cloud-native data warehouse.

Data 98

Data Technical Review Storage Systems Review 98

Existek

JANUARY 11, 2022

It’ll be interesting to discover how market-leading providers set new standards and alter approaches to cloud computing. Azure vs AWS market share. AZURE VS AWS MARKET SHARE. It might be challenging as it requires a detailed analysis of the business-specific needs and a deeper understanding of the overall market situation. .

Azure 52

Azure AWS Cloud How To 52

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 Artificial Inteligence 98

Cloudera

SEPTEMBER 29, 2020

A TPC-DS 10TB dataset was generated in ACID ORC format and stored on the ADLS Gen 2 cloud storage. Cloudera Data Warehouse vs HDInsight. Finally, CDW is offered in CDP along with other data lifecycle services – Data Engineering, Operational Database, Machine Learning, and Data Hub. Conclusion.

Azure 122

Azure Data Comparison Virtualization 122

Exadel

FEBRUARY 20, 2023

Amazon For Cloud Artificial Intelligence Amazon began by making storage and virtual machines. Vertex AI leverages a combination of data engineering, data science, and ML engineering workflows with a rich set of tools for collaborative teams. Let’s race through these vendors and see what they’re up to.

Artificial Inteligence 52

Artificial Inteligence Cloud Machine Learning Azure 52

Cloudera

DECEMBER 20, 2022

In that sense, access to the auction is more conservative and can lead to inefficiencies in the use of assets, as some of the energy produced may be discarded if it is not saleable in the intraday market. Lastly, we examine retail companies, the energy marketers. Towards a better customer experience.

Energy 85

Energy Analytics Data Artificial Inteligence 85

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

Cloudera

NOVEMBER 16, 2022

There is a clear consensus that data teams should express their goals and results in business value terms and not in technical, tactical descriptions, such as “improving data engineering” and “better master data management.” . Yet if their only purpose is secure data storage, they know the market will leave them behind.

Data 85

Data Insurance Metrics eBook 85

Altexsoft

AUGUST 25, 2020

Prominent enterprises in numerous sectors including sales, marketing, research, and healthcare are actively collecting big data. 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.

Big Data 75

Big Data Data Engineering Engineering Data 75

KitelyTech

FEBRUARY 3, 2021

In this article, we discussed the most popular big data and data science development services that your company can take advantage of. User Data Collection. User data collection is data about a user who is collected for market research purposes. Database Management.

Big Data 52

Big Data Data Development Business Intelligence 52

Cloudera

JANUARY 20, 2021

Digital transformation is a hot topic for all markets and industries as it’s delivering value with explosive growth rates. Data Enrichment – data pipeline processing, aggregation & management to ready the data for further refinement. STEP 3: Monitor data throughput from each factory.

Data 113

Data Artificial Inteligence Analytics Machine Learning 113

Hu's Place - HitachiVantara

JANUARY 3, 2019

Data curation will be a focus to understand the meaning of the data as well as the technologies that are applied to the data so that data engineers can move and transform the essential data that data consumers need to power the organization.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

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

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. Opportunity 3: Retire personal datasets and fit-for-purpose data marts.

Sustainability 46

Sustainability Virtualization Data Energy 46

Altexsoft

AUGUST 19, 2022

So, in this article, we’ll talk about the current BI market leader – Microsoft Power BI – and discuss its functionality, pros and cons, and alternatives. Microsoft Power BI is an interactive data visualization software suite developed by Microsoft that helps businesses aggregate, organize, and analyze data. What is Power BI?

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

Altexsoft

SEPTEMBER 9, 2022

These are important for SEO purposes because search engines use them to “understand” what the page is about and how it has to be displayed and ranked in search results. Besides, they have to attract users to visit the website, so it’s a powerful marketing tool. It aims at making data assets understandable and discoverable for users.

Tools 59

Tools Technical Review Software Review Systems Review 59

Altexsoft

JUNE 14, 2021

Their technology works by scanning the market, looking for discounted and lowered prices of insurance premiums for their clients. You can start investing in data infrastructure and analytical pipelines to automate data collection and analysis mechanisms. You’ll need a data engineering team for that.

Insurance 88

Insurance Artificial Inteligence Machine Learning Policies 88

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. To buy or build?

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Cloudera

SEPTEMBER 17, 2020

For enterprise organizations, managing and operationalizing increasingly complex data across the business has presented a significant challenge for staying competitive in analytic and data science driven markets. CDP data lifecycle integration and SDX security and governance.

Data Engineering 105

Data Engineering Engineering Data Tools 105

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

Altexsoft

OCTOBER 29, 2020

But before you dive in, we recommend you reviewing our more beginner-friendly articles on data transformation: Complete Guide to Business Intelligence and Analytics: Strategy, Steps, Processes, and Tools. What is Data Engineering: Explaining the Data Pipeline, Data Warehouse, and Data Engineer Role.

Testing 63

Testing Tools Software Review Technical Review 63

Kentik

MAY 23, 2017

Imagine a big data time-series datastore that unifies traffic flow records (NetFlow, sFlow, IPFIX) with related data such as BGP routing, GeoIP, network performance, and DNS logs, that retains unsummarized data for months, and that has the compute and storage power to answer ad hoc queries across billions of data points in a couple of seconds.

Network 40

Network Open Source Big Data Load Balancer 40

TechCrunch

NOVEMBER 4, 2022

Ashish Kakran , principal at Thomvest Ventures , is a product manager/engineer turned investor who enjoys supporting founders with a balance of technical know-how, customer insights, empathy with challenges and market knowledge. Each step of the data analysis process is ripe for disruption. More posts by this contributor.

Data 218

Data Storage Analytics Data Engineering 218

Strategy Driven

AUGUST 27, 2015

The Internet and cloud computing have revolutionized the nature of data capture and storage, tempting many companies to adopt a new 'Big Data' philosophy: collect all the data you can; all the time.

Big Data 33

Big Data Data Storage Journal 33

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

Mobilunity

NOVEMBER 24, 2021

In most digital spheres, especially in fintech, where all business processes are tied to data processing, a good big data engineer is worth their weight in gold. In this article, we’ll discuss the role of an ETL engineer in data processing and why businesses need such experts nowadays. Who Is an ETL Engineer?

Development 52

Development Storage Recruiting Architecture 52

CIO

JULY 15, 2022

The forecasting systems DTN had acquired were developed by different companies, on different technology stacks, with different storage, alerting systems, and visualization layers. Working with his new colleagues, he quickly identified rebuilding those five systems around a single forecast engine as a top priority.

Systems Review 279

Systems Review Fractional CTO System Development Team Review 279

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 66

Cloud Data Load Balancer Storage 66