Hu's Place - HitachiVantara

JANUARY 3, 2019

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Building an AI or machine learning model is not a one-time effort.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

O'Reilly Media - Ideas

FEBRUARY 11, 2019

Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning. In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Deep Learning.

Big Data 87

Big Data Technology Tools Cloud 87

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

Erik Bernhardsson

OCTOBER 27, 2015

If you want to understand the business and generate actionable insights, then in my experience you need pretty much no knowledge of statistics and machine learning. So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering. It’s very different. and much more.

Data 100

Data Big Data Machine Learning Artificial Inteligence 100

Erik Bernhardsson

OCTOBER 27, 2015

If you want to understand the business and generate actionable insights, then in my experience you need pretty much no knowledge of statistics and machine learning. So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering. It’s very different. and much more.

Data 100

Data Big Data Machine Learning Artificial Inteligence 100

Cloudera

DECEMBER 10, 2020

CDW is built on top of CDP and has many cutting edge features to provide an excellent data warehousing user experience. For an overview of CDW’s architecture, see DW Built for the cloud. CDW architecture is elastic and simplifies capacity planning. Which technical capabilities make CDW cost-efficient?

Data 98

Data Technical Review Storage Systems Review 98

Cloudera

AUGUST 31, 2021

It outperforms other data warehouses on all sizes and types of data, including structured and unstructured, while scaling cost-effectively past petabytes. Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics.

Data 85

Data Analytics Cloud Technical Review 85

Cloudera

SEPTEMBER 9, 2022

The promise of a modern data lakehouse architecture. Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested.

Architecture 103

Architecture Innovation Data Open Source 103

Cloudera

OCTOBER 19, 2023

While navigating so many simultaneous data-dependent transformations, they must balance the need to level up their data management practices—accelerating the rate at which they ingest, manage, prepare, and analyze data—with that of governing this data.

Data 78

Data Compliance Architecture Data Engineering 78

Cloudera

AUGUST 30, 2022

Data quality issues deter trust and hinder accurate analytics. Disparate systems create issues with transparency and compliance. Modern data architectures. To eliminate or integrate these silos, the public sector needs to adopt robust data management solutions that support modern data architectures (MDAs).

Architecture 80

Architecture Data Artificial Inteligence Artificial Intelligence 80

Altexsoft

SEPTEMBER 9, 2022

Today, such modern data management frameworks as DataOps strongly rely on effective metadata capture and management to bring order into the chaotic data flows. Plus, a data fabric architecture design approach is also based on metadata as one of the main building blocks. Metadata management components.

Tools 59

Tools Technical Review Software Review Systems Review 59

Cloudera

OCTOBER 1, 2022

According to Gartner, by 2023 65% of the world’s population will have their personal data covered under modern privacy regulations. . As a result, growing global compliance and regulations for data are top of mind for enterprises that conduct business worldwide. People selling information.

Government 76

Government Artificial Inteligence Enterprise Strategy 76

Altexsoft

JUNE 6, 2019

Forecasting demand with machine learning in Walmart. Systems that rely on machine learning are capable of analyzing a multitude of data points, finding subtle patterns (indicating changes in customer preferences, behavior, or satisfaction) which can be non-obvious for a human. Source: Lenovo StoryHub.

Artificial Inteligence 89

Artificial Inteligence Artificial Intelligence Retail Innovation 89

Altexsoft

JUNE 11, 2021

an also be described as a part of business process management (BPM) that applies data science (with its data mining and machine learning techniques) to dig into the records of the company’s software, get the understanding of its processes performance, and support optimization activities. Process mining ?an

Applications 69

Applications Weak Development Team Systems Review Software Review 69

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 coding practices when you’re letting GitHub Copilot write your code for you? This has been a strange year.

Trends 118

Trends Technical Review Technology Artificial Inteligence 118

Altexsoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Traditional data warehouse platform architecture. Data lake. Data lake architecture example.

Architecture 52

Architecture Data Storage Machine Learning 52

ParkMyCloud

JANUARY 17, 2020

Display a basic understanding of core AWS services, uses, and basic AWS architecture best practices. Additionally, they must be able to implement and automate security controls, governance processes, and compliance validation. Design and maintain network architecture for all AWS services. Design and maintain Big Data.

Google Cloud 9

Google Cloud Azure AWS Cloud 9

CIO

AUGUST 18, 2022

Traditionally, organizations have maintained two systems as part of their data strategies: a system of record on which to run their business and a system of insight such as a data warehouse from which to gather business intelligence (BI). You can intuitively query the data from the data lake.

Data 349

Data Artificial Inteligence Technical Advisors Technical Review 349

Altexsoft

AUGUST 22, 2022

What’s more, Gartner identifies data fabric implementation as one of the top strategic technology trends for 2022 and expects that by 2024, data fabric deployments will increase the efficiency of data use while halving human-driven data management tasks. What is data fabric? Data fabric architecture example.

Architecture 52

Architecture Artificial Inteligence Technical Review Data 52

Cloudera

JUNE 30, 2022

Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP) — including Cloudera Data Warehousing ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ).

Data 89

Data Analytics Machine Learning Artificial Inteligence 89

Altexsoft

FEBRUARY 8, 2021

To support the planning process, predictive analytics and machine learning (ML) techniques can be implemented. We have previously described demand forecasting methods and the role of machine learning solutions in a dedicated article. Comparison between traditional and machine learning approaches to demand forecasting.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

BPM

JANUARY 18, 2019

Now we’re seeing AI dominating the conversations (without a lot of actual adoption in the early majority), and RPA creating a lot of buzz, though companies adopting it are starting to realize that scaling an RPA based automation architecture is flawed by design. This requires a new architecture capable of true scale and speed.

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

John Snow Labs

MAY 24, 2023

Almost 90% of the machine learning models encounter delays and never make it into production. Developing a machine learning model requires a big amount of training data. Therefore, the data needs to be properly labeled/categorized for a particular use case.

Tools 52

Tools Machine Learning Artificial Inteligence Software Review 52

Altexsoft

NOVEMBER 9, 2021

Traditional statistical methods use mainly internal, historical data to predict trends within relatively stable markets. Meanwhile, machine learning (ML) techniques are capable of processing a wide range of both historical and current data from multiple external and internal sources. Compliance evaluation.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

JANUARY 14, 2021

We’ll review all the important aspects of their architecture, deployment, and performance so you can make an informed decision. Before jumping into the comparison of available products right away, it will be a good idea to get acquainted with the data warehousing basics first. Data warehouse architecture.

Backup 115

Backup Azure Software Review Systems Review 115

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

Altexsoft

MARCH 18, 2021

Transformations may include: data sorting and filtering to get rid of irrelevant items, de-duplicating and cleansing, translating and converting, removing or encrypting to protect sensitive information, splitting or joining tables, etc. These are data engineers who are responsible for implementing these processes. Compliance.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Altexsoft

MARCH 2, 2023

Data governance , on the other hand, focuses on the strategic and organizational aspects of managing data, such as establishing policies and procedures for data use and ensuring compliance with regulatory requirements. Learn how data is prepared for machine learning in our dedicated video.

Government 52

Government Tools Data Weak Development Team 52

Cloudera

DECEMBER 21, 2020

Instead, it is a move towards recognizing those companies that are driving innovation and agility by modernizing their data architecture and optimizing their infrastructure to leverage invaluable insights. This year’s winner, the West Midlands Police, stood out with its groundbreaking data strategy that did both of these things.

Data 97

Data Technical Review Cloud Enterprise 97

Cloudera

OCTOBER 25, 2022

Enabling data and analytics in the cloud allows you to have infinite scale and unlimited possibilities to gain faster insights and make better decisions with data. 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

Cloudera

FEBRUARY 11, 2019

To achieve their goals of digital transformation and becoming data-driven, companies need more than just a better data warehouse or BI tool. They need a range of analytical capabilities from data engineering to data warehousing to operational databases and data science. Governing for compliance.

Government 59

Government Compliance Machine Learning Artificial Inteligence 59

OpenCredo

MARCH 20, 2020

Security, because we work with sensitive data, client information and intellectual property. We cannot afford for this data to make its way into the wrong hands and must comply with compliance mandates. By making data security usable we also expect to make systems easier to manage, operate and understand. Challenges.

Cloud 40

Cloud Data Weak Development Team Compliance 40

Cloudera

DECEMBER 7, 2017

Cloud promises many advantages as an environment for machine learning and analytics. Cloud offers elasticity of those resources to efficiently support transient analytics workloads and data pipelines. That data may be used in ways that don’t comply with appropriate security and governance policies. It’s time for SDX.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

O'Reilly Media - Ideas

MARCH 1, 2023

Content about software development was the most widely used (31% of all usage in 2022), which includes software architecture and programming languages. Software development is followed by IT operations (18%), which includes cloud, and by data (17%), which includes machine learning and artificial intelligence.

Trends 136

Trends Technical Review Technology Software Review 136

Cloudera

AUGUST 24, 2020

Next you need to understand your data , cataloging enterprise data into business and compliance terms. The most relevant use cases are those that use or integrate data, including publicly available data, and you combine that with your internal data.

Cloud 80

Cloud Data Government Innovation 80

Cloudera

OCTOBER 11, 2021

Modak, a leading provider of modern data engineering solutions, is now a certified solution partner with Cloudera. Customers can now seamlessly automate migration to Cloudera’s Hybrid Data Platform — Cloudera Data Platform (CDP) to dynamically auto-scale cloud services with Cloudera Data Engineering (CDE) integration with Modak Nabu.

Data Engineering 95

Data Engineering Engineering Data Cloud 95

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. Modern architecture. Elastic architecture. Cloudera Data Platform Architecture.

Data 63

Data Pharmaceuticals Open Source Machine Learning 63