CTOvision

MAY 1, 2014

We are looking for a talented Big Data Software Engineer to join the Applied Intelligence group in San Francisco. Applied Intelligence derives actionable intelligence from our data to optimize massive scale operation of datacenters worldwide. Work collaboratively to deliver data in visually impactful ways.

Big Data 107

Big Data Data Engineering Engineering Data Center 107

O'Reilly Media - Ideas

JUNE 4, 2019

Get hands-on training in Docker, microservices, cloud native, Python, machine learning, and many other topics. Learn new topics and refine your skills with more than 219 new live online training courses we opened up for June and July on the O'Reilly online learning platform. AI and machine learning.

Course 65

Course Training Artificial Inteligence Software Review 65

Capgemini

JANUARY 17, 2023

Here are four resolutions to make your data strategy pay off this year. Reassess your data architecture. Most executives (72 percent) say that data, both fragmented and with poor quality, is likely to be the biggest issue when aspiring to achieve AI goals. This has sped up the time for insights for all groups.

Strategy 52

Strategy Technical Review Data Artificial Inteligence 52

Cloudera

JANUARY 15, 2021

Cloudera Data Warehouse is a highly scalable service that marries the SQL engine technologies of Apache Impala and Apache Hive with cloud-native features to deliver best-in-class price-performance for users running data warehousing workloads in the cloud. Overview of Cloudera Data Warehouse. Benchmark Description.

Performance 91

Performance Cloud Data Storage 91

Cloudera

DECEMBER 21, 2020

Company data exists in the data lake. Data Catalog profilers have been run on existing databases in the Data Lake. A Cloudera Machine Learning Workspace exists . A Cloudera Data Warehouse virtual warehouse with Cloudera Data Visualisation enabled exists. The Data Scientist.

Data 97

Data Virtualization Banking Data Engineering 97

O'Reilly Media - Ideas

MAY 1, 2019

Get hands-on training in machine learning, blockchain, cloud native, PySpark, Kubernetes, and many other topics. Learn new topics and refine your skills with more than 160 new live online training courses we opened up for May and June on the O'Reilly online learning platform. AI and machine learning.

Course 45

Course Training Artificial Inteligence Machine Learning 45

O'Reilly Media - Ideas

JUNE 5, 2019

Get hands-on training in Docker, microservices, cloud native, Python, machine learning, and many other topics. Learn new topics and refine your skills with more than 219 new live online training courses we opened up for June and July on the O'Reilly online learning platform. AI and machine learning.

Course 49

Course Training Artificial Inteligence Software Review 49

O'Reilly Media - Ideas

DECEMBER 6, 2018

To assess the state of adoption of machine learning (ML) and AI, we recently conducted a survey that garnered more than 11,000 respondents. Resurgence in deep learning began in 2011/2012 with record-setting models for speech recognition and computer vision. had a national surplus of people with data science skills.

Technology 76

Technology Artificial Inteligence Machine Learning Hardware 76

Apiumhub

AUGUST 12, 2021

He has spoken at many conferences, user groups, etc., Gema Parreño Piqueras – Lead Data Science @Apiumhub Gema Parreno is currently a Lead Data Scientist at Apiumhub, passionate about machine learning and video games, with three years of experience at BBVA and later at Google in ML Prototype. Twitter: ??

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Existek

SEPTEMBER 28, 2023

Developers often have specialized roles based on their areas of expertise, like machine learning, computer vision, natural language processing, deep learning, robotics process automation, etc. Developers often work with data acquisition, data cleaning, data transformation, and data augmentation.

Artificial Inteligence 52

Artificial Inteligence Development How To Artificial Intelligence 52

Altexsoft

JUNE 26, 2023

While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Altexsoft

JUNE 25, 2019

Being at the top of data science capabilities, machine learning and artificial intelligence are buzzing technologies many organizations are eager to adopt. 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.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

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

Confluent

JUNE 18, 2019

Using SQL to run your search might be enough for your use case, but as your project requirements grow and more advanced features are needed—for example, enabling synonyms, multilingual search, or even machine learning—your relational database might not be enough. Building an indexing pipeline at scale with Kafka Connect.

Scalability 95

Scalability Architecture Machine Learning Artificial Inteligence 95

Altexsoft

SEPTEMBER 9, 2022

In data science , metadata is one of the central aspects: It describes data (including unstructured data streams) fed into a big data analytical platform, capturing, for example, formats, file sizes, source of information, permission details, etc. Types of metadata. There are multiple ways to categorize metadata.

Tools 59

Tools Technical Review Software Review Systems Review 59

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 116

Trends Technical Review Technology Artificial Inteligence 116

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. Adopting AI can help data quality.

Weak Development Team 127

Weak Development Team Data Technical Review Survey 127

Cloudera

FEBRUARY 22, 2022

Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists.

Data 105

Data Analytics Disaster Recovery Travel 105

Altexsoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. How data engineering works in a nutshell.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Altexsoft

JANUARY 22, 2020

As a result, it became possible to provide real-time analytics by processing streamed data. Please note: this topic requires some general understanding of analytics and data engineering, so we suggest you read the following articles if you’re new to the topic: Data engineering overview.

Analytics 20

Analytics Data IoT Analysis 20

Cloudera

APRIL 26, 2023

Can you achieve similar outcomes with your on-premises data platform? Application modernization initiatives have led to cloud native architectures gaining popularity on premises, making it a sensible choice to extend to your data platform. This is exactly where cloud native architectures excel, and why they are so popular.

Cloud 64

Cloud Data Load Balancer Storage 64

Altexsoft

FEBRUARY 8, 2021

It includes a broad range of tightly interrelated activities that we can arrange into several major groups. To support the planning process, predictive analytics and machine learning (ML) techniques can be implemented. Comparison between traditional and machine learning approaches to demand forecasting.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

O'Reilly Media - Ideas

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Why aren’t traditional software tools sufficient?

Government 80

Government Artificial Inteligence Machine Learning Testing 80

TIBCO - Connected Intelligence

NOVEMBER 4, 2019

TDWI Munich , with thousands of attendees and nearly a hundred sponsors, is Germany’s premier annual data and analytics event. I was fortunate to be invited to speak about artificial intelligence and machine learning there this year. Me speaking at my session, Fueling Artificial Intelligence & Machine Learning with Data.

Virtualization 4

Virtualization Artificial Inteligence Data Artificial Intelligence 4

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

Altexsoft

NOVEMBER 7, 2022

You can hardly compare data engineering toil with something as easy as breathing or as fast as the wind. The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. How data engineering works. Airflow architecture.

Weak Development Team 52

Weak Development Team Technical Review Software Review Systems 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

APRIL 26, 2022

We’ll dive deeper into Snowflake’s pros and cons, its unique architecture, and its features to help you decide whether this data warehouse is the right choice for your company. Data warehousing in a nutshell. BTW, we have an engaging video explaining how data engineering works.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

O'Reilly Media - Ideas

MARCH 1, 2023

For the purposes of this report, units viewed is always normalized to 1, where 1 is assigned to the greatest number of units in any group of topics. Content about software development was the most widely used (31% of all usage in 2022), which includes software architecture and programming languages.

Trends 134

Trends Technical Review Technology Software Review 134

Altexsoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Big Data 59

Big Data Data Google Cloud Storage 59

Altexsoft

MARCH 31, 2020

When it comes to building and managing infrastructure for data movement and its strategic usage, that’s the duty of data engineers. Data pipeline components. To understand how the data pipeline works in general, let’s see what a pipeline usually consists of. Data pipeline components.

Data 76

Data Storage Analytics Data Center 76

Cloudera

AUGUST 20, 2021

The Cloudera Data Platform comprises a number of ‘data experiences’ each delivering a distinct analytical capability using one or more purposely-built Apache open source projects such as Apache Spark for Data Engineering and Apache HBase for Operational Database workloads.

Strategy 95

Strategy Data Technical Review Weak Development Team 95

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. Operational KPIs.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Netflix Tech

NOVEMBER 20, 2023

These challenges are currently addressed in suboptimal and less cost efficient ways by individual local teams to fulfill the needs, such as Lookback: This is a generic and simple approach that data engineers use to solve the data accuracy problem. Users configure the workflow to read the data in a window (e.g.

Windows 84

Windows Software Review Data Engineering 84

Cloudera

APRIL 9, 2021

However, legacy data warehouses have limited support for public and private cloud deployment architectures. Managing disparate solutions is operationally inefficient, provides an inconsistent user experience and often leads to increased risk due to a lack of common security, governance and lineage of data. . Efficiency.

Data 58

Data Software Review Technical Review Architecture 58

OpenCredo

MARCH 20, 2020

Naturally, we wish to prevent unauthorised data access; exfiltration attacks can occur when sensitive datasets are maliciously or accidentally transferred outside of your organisation by users who are authorised to access the data. Data Engineering. Interpretability and Explainability.

Cloud 40

Cloud Data Weak Development Team Compliance 40