Altexsoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Architecture 52

Architecture Data Storage Machine Learning 52

Capgemini

NOVEMBER 14, 2022

For self-service BI to succeed, however, the entire data value chain may need to be fixed. Data has to be easy to find, understand, access, and use for everyone in the chain: data engineers, analysts, data scientists, and business users. It needed to teach people to fish in the data lake.

Agile 52

Agile Technical Review Systems Review Artificial Intelligence 52

Altexsoft

JUNE 11, 2021

Process mining is a set of techniques for the analysis of operational processes based on event logs extracted from company’s databases, information systems, or business management software such as enterprise resource planning (ERP), customer relationship management (CRM), electronic health records (EHR), etc. Ensure data quality.

Applications 69

Applications Weak Development Team Systems Review Software Review 69

Altexsoft

SEPTEMBER 9, 2022

Efficient metadata management ensures data integrity , consistency, trustworthiness, and compliance. To ensure metadata quality as well as correct application of metadata policies and proper compliance to requirements and standards, audits have to be regularly conducted by data stewards.

Tools 59

Tools Technical Review Software Review Systems Review 59

O'Reilly Media - Ideas

NOVEMBER 13, 2018

The Future of Privacy Forum and Immuta recently released a report with some great suggestions on how one might approach machine learning projects with risk management in mind: When you’re working on a machine learning project, you need to employ a mix of data engineers, data scientists, and domain experts.

Machine Learning 89

Machine Learning Artificial Inteligence Software Review Conference 89

Altexsoft

FEBRUARY 8, 2021

The data that your procurement management software generates can help you analyze potential suppliers’ performance by comparing their KPIs, prices, compliance, and other variables. For example, you can see that the machine breakdown increased, and the root cause analysis shows that it happened because of cheaper spare parts or fuel.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

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. . Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data 82

Data Analytics Cloud Technical Review 82

Hu's Place - HitachiVantara

JANUARY 3, 2019

In order to utilize the wealth of data that they already have, companies will be looking for solutions that will give comprehensive access to data from many sources. More focus will be on the operational aspects of data rather than the fundamentals of capturing, storing and protecting data.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

TIBCO - Connected Intelligence

OCTOBER 15, 2020

Towards these trends, our clients have significantly invested in data-related technologies and talent over the past several years, including: Data governance to cater to enterprise-level financial reporting and regulatory compliance as an example. Self-service data discovery (e.g.,

Virtualization 50

Virtualization Data Energy Innovation 50

Lacework

NOVEMBER 1, 2023

In many cases we see that customers prefer to have their data stored and managed locally in their home region, both for reasons of regulatory compliance and also business preference. A mart is a group of aggregated tables (e.g.,

System Design 52

System Design Systems Review System Data 52

Cloudera

DECEMBER 10, 2020

These file formats not only help avoid data duplication into proprietary storage formats but also provide highly efficient storage formats. Multiple analytical engines (data warehousing, machine learning, data engineering, and so on) can operate on the same data in these file formats.

Data 98

Data Technical Review Storage Systems Review 98

O'Reilly Media - Ideas

FEBRUARY 11, 2019

Temporal data and time-series analytics. Text and Language processing and analysis. Foundational data technologies. Machine learning and AI require data—specifically, labeled data for training models. Data Platforms. Data Integration and Data Pipelines. Automation in data science and big data.

Big Data 84

Big Data Technology Tools Cloud 84

Cloudera

NOVEMBER 12, 2020

Typically, when we talk about data warehousing at an enterprise level on the cloud, one of the biggest concerns is that moving workloads from on-premises to the cloud is not seamless and opens up new risks for data safety and security. The last thing you need is another 1-3 month analysis project. Key areas of concern are: .

Data Center 101

Data Center Cloud Data Policies 101

Cloudera

MAY 22, 2018

Access any data anywhere – from cloud object storage to data warehouses, CDSW provides connectivity not only to CDH but the systems your data science teams rely on for analysis. This leads to wasted time and effort during research and collaboration or, worse, compliance risk.

Data 41

Data Load Balancer Machine Learning Artificial Inteligence 41

DataRobot

MARCH 10, 2022

With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.

Artificial Inteligence 52

Artificial Inteligence Artificial Intelligence Machine Learning IoT 52

Cloudera

FEBRUARY 15, 2024

While there are clear reasons SVB collapsed, which can be reviewed here , my purpose in this post isn’t to rehash the past but to present some of the regulatory and compliance challenges financial (and to some degree insurance) institutions face and how data plays a role in mitigating and managing risk.

Insurance 83

Insurance Compliance Technical Review Banking 83

Existek

JANUARY 11, 2022

It might be challenging as it requires a detailed analysis of the business-specific needs and a deeper understanding of the overall market situation. . Security, identity, and compliance. Choosing cloud-based solutions is also about getting great data, account, and workload protection. Development Operations Engineer $122 000.

Azure 52

Azure AWS Cloud How To 52

Cloudera

OCTOBER 19, 2023

Add to this, too, the difficulty of integrating potentially dissimilar compliance frameworks: for example, separate telcos might be operating under different regulatory guidelines, appropriate to specific jurisdictions or business practices, requiring the merged entity to formalize a single, unified framework for compliance.

Data 74

Data Compliance Architecture Data Engineering 74

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

Altexsoft

NOVEMBER 9, 2021

If you use a separate RFI (Request for information), RFP (Request for Proposal), or RFQ (Request for Quote) platform, you’ll have to integrate it with your ERP or procurement system to collect the necessary supplier data. Price analysis dashboard by Sievo with opportunities overview. Compliance evaluation. Fraud detection.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

O'Reilly Media - Ideas

MARCH 31, 2020

It may even be necessary to do image or video analysis to make content-based recommendations, detect fraud, or reject content that violates your rules (for example, live shooter videos). To make these judgements, an AI product manager needs to understand the company’s data inside and out. How do you select what to work on?

Product Management 135

Product Management Artificial Inteligence Machine Learning Software Review 135

CIO

AUGUST 18, 2022

The data warehouse requires a time-consuming extract, transform, and load (ETL) process to move data from the system of record to the data warehouse, whereupon the data would be normalized, queried, and answers obtained. based Walgreens consolidated its systems of insight into a single data lakehouse.

Data 348

Data Artificial Inteligence Technical Advisors Technical Review 348

O'Reilly Media - Ideas

JANUARY 25, 2024

While our analysis of search trends shows that interest in ChatGPT has peaked among our platform’s users, interest in natural language processing (NLP) showed a 195% increase—and from a much higher starting point. Data analysis and databases Data engineering was by far the most heavily used topic in this category; it showed a 3.6%

Trends 114

Trends Technical Review Technology Artificial Inteligence 114

Altexsoft

MARCH 18, 2021

ETL and ELT are the most widely applied approaches to deliver data from one or many sources to a centralized system for easy access and analysis. These are data engineers who are responsible for implementing these processes. Moreover, raw data can undergo numerous transformations as it is stored indefinitely.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

ParkMyCloud

JANUARY 17, 2020

Additionally, they must be able to implement and automate security controls, governance processes, and compliance validation. For individuals who perform complex Big Data analyses and have at least two years of experience using AWS. Implement core AWS Big Data services according to basic architecture best practices.

Google Cloud 9

Google Cloud Azure AWS Cloud 9

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

OpenCredo

MARCH 20, 2020

It is used extensively across the business for reporting, analysis and powering services. To optimise our use of data, we need services which store it reliably, provide interfaces for analysis and automate transformation. Security, because we work with sensitive data, client information and intellectual property.

Cloud 40

Cloud Data Weak Development Team Compliance 40

Altexsoft

MARCH 16, 2021

It’s referred to as big data, since, for example, just a single complete human genome sequence produces about 200 Gb of raw data. Now, we know where big data in healthcare originates. Check our article on data engineering to get a detailed understanding of the data pipeline and its components.

Big Data 116

Big Data Healthcare Applications Data 116

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

DataRobot

MARCH 16, 2023

To work effectively, data scientists need agility in the form of access to enterprise data, streamlined tooling, and infrastructure that just works. Agility and enterprise security, compliance, and governance are often at odds. Join us on March 16 and see more of the DataRobot and Snowflake integration first hand!

Data 52

Data Machine Learning Artificial Inteligence Agile 52

Altexsoft

MARCH 2, 2023

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. Compliance. Data governance provides a platform for meeting government regulations like GDPR or HIPAA. Improved data management.

Government 52

Government Tools Data Weak Development Team 52

Cloudera

OCTOBER 25, 2022

“They combine the best of both worlds: flexibility, cost effectiveness of data lakes and performance, and reliability of data warehouses.”. It allows users to rapidly ingest data and run self-service analytics and machine learning. It’s a critical part of your security posture and your first line of defense.

Cloud 52

Cloud Data Firewall AWS 52

O'Reilly Media - Ideas

MARCH 18, 2020

More than half of respondent organizations identify as “mature” adopters of AI technologies: that is, they’re using AI for analysis or in production. One-sixth of respondents identify as data scientists, but executives—i.e., What is more, almost three-quarters of survey respondents say they work with data in their jobs.

Enterprise 131

Enterprise Survey Technical Review Weak Development Team 131

Altexsoft

NOVEMBER 5, 2021

So, we’ll only touch on its most vital aspects, instruments, and areas of interest — namely, data quality, patient identity, database administration, and compliance with privacy regulations. Data analysis, transformation, and decision support revolve around deriving knowledge and insights critical for enhancing patient care.

Technical Review 59

Technical Review Technology Software Review Healthcare 59

Altexsoft

JANUARY 14, 2021

A data warehouse is often abbreviated as DW or DWH. You may also find it under the name of an enterprise data warehouse (EDW). It is usually created and used primarily for data reporting and analysis purposes. As such, it is possible to retrieve old archived data if needed. Data warehouse architecture.

Backup 115

Backup Azure Software Review Systems Review 115

Altexsoft

JULY 19, 2022

The examples of business use cases consuming data from the analytical plane are dynamic pricing , sentiment analysis , and product and content recommendations. The divide of data and teams. Data producers lack ownership over the information they generate which means they are not in charge of its quality.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

SEPTEMBER 23, 2019

Why would you need a detailed business analysis? In this article, we’ll explain what business analysis in software development is and the role of a business analyst (BA) within this process. Strong background with different analytical techniques including Interface Analysis, Feasibility Analysis, and SWOT Analysis is required.

Weak Development Team 62

Weak Development Team Software Development Research Project Management 62