Cloudera

FEBRUARY 9, 2021

In this blog post, we’ll look at both Apache HBase and Apache Phoenix concepts relevant to developing applications for Cloudera Operational Database. But first, let’s look at the different form factors in which Cloudera Operational Database is available to developers: Public cloud: CDP Data Hub Operational Database template .

Applications 103

Applications Development Storage Database Administration 103

CIO

FEBRUARY 14, 2023

Streaming data technologies unlock the ability to capture insights and take instant action on data that’s flowing into your organization; they’re a building block for developing applications that can respond in real-time to user actions, security threats, or other events. The application will contain ML mathematical algorithms.

Artificial Inteligence 288

Artificial Inteligence Strategy Data Machine Learning 288

CIO

JUNE 14, 2023

CDP Generalist The Cloudera Data Platform (CDP) Generalist certification verifies proficiency with the Cloudera CDP platform. The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect.

Big Data 317

Big Data Analytics Data eLearning 317

CIO

FEBRUARY 13, 2023

In one use case, AR and VR are being used to re-create people’s spines in a model so that surgeons can look at them in advance of surgeries to help them perform better, says Peter Fleischut, group senior vice president and chief information and transformation officer.

Budget 363

Budget Artificial Inteligence Technical Review VR 363

The Crazy Programmer

NOVEMBER 6, 2021

A cloud architect has a profound understanding of storage, servers, analytics, and many more. Big Data Engineer. Another highest-paying job skill in the IT sector is big data engineering. And as a big data engineer, you need to work around the big data sets of the applications.

Artificial Inteligence 290

Artificial Inteligence Artificial Intelligence Blockchain Software Review 290

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

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 336

Analytics Data Analysis Business Analytics 336

Cloudera

JUNE 26, 2023

Here are some tips and tricks of the trade to prevent well-intended yet inappropriate data engineering and data science activities from cluttering or crashing the cluster. For data engineering and data science teams, CDSW is highly effective as a comprehensive platform that trains, develops, and deploys machine learning models.

Tools 78

Tools Data Engineering Analytics Testing 78

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

Kentik

APRIL 12, 2023

With modern networks’ increasing complexity and scale, it has become essential to collect and analyze data from various sources to gain insights into network performance, security, and availability. DataOps team roles In a DataOps team, several key roles work together to ensure the data pipeline is efficient, reliable, and scalable.

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

Cloudera

FEBRUARY 1, 2024

Delivering transformational innovation and accurate business decisions requires harnessing the full potential of your organization’s entire data ecosystem. Ultimately, this boils down to how reliable and trustworthy the underlying data that feeds your insights and applications is.

Backup 84

Backup Cloud Architecture Resources 84

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

Altexsoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Architecture 52

Architecture Data Storage Machine Learning 52

Dzone - DevOps

FEBRUARY 16, 2023

So in this article, I will talk about how I improved overall data processing efficiency by optimizing the choice and usage of data warehouses. Too Much Data on My Plate The choice of data warehouses was never high on my worry list until 2021. In the company's infancy, we didn't have too much data to juggle.

Data 71

Data ChatGPT Storage Architecture 71

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

JULY 24, 2023

That first step requires integrating the latest versions of all required open source projects, including not just data processing engines (e.g., Apache Impala, Apache Spark) but also all foundational services needed for storage (e.g., data engineering pipelines, machine learning models).

Open Source 67

Open Source Analytics Software Review Metrics 67

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 85

Machine Learning Artificial Inteligence Weak Development Team Construction 85

Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

Cloudera

MAY 17, 2021

Yet for organizations that only want to get their toes wet and perhaps just evaluate the capability, the 16 cores, 128 GB RAM, and 600 GB of storage prevented them from doing just that. we introduce detailed low resource requirements that reduce the amount of CPU, RAM, and storage needed by up to 75%. With Private Cloud 1.2,

Cloud 89

Cloud Machine Learning Artificial Inteligence Storage 89

Cloudera

SEPTEMBER 1, 2020

It is hard to believe if you have had previous experience with setting up, sizing, and deploying a distributed search engine service that this is possible. Imagine how many times IT has lost valuable time spending hours trying to understand Apache Solr application requirements and map them into how to best size and deploy the Solr service.

Data 69

Data Backup Disaster Recovery Storage 69

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Performance.

Big Data 63

Big Data Data Storage Microservices 63

Apps Associates

JULY 28, 2022

Use Case 1: Data integration for big data, data lakes, and data science. Efficiently load and transform data at scale into Data Lakes for data science and analytics. Load the data into object storage and create high-quality models more quickly using OCI data science. OCPU hours.

Infrastructure 52

Infrastructure Cloud Data Linux 52

Mentormate

OCTOBER 31, 2023

For example, if the problem is predicting patient readmissions in healthcare, one approach is to analyze electronic health records, while another might involve real-time monitoring data. Furthermore, it’s essential to compare the benefits of using a pre-trained model, if applicable, or training one from scratch.

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

Cloudera

DECEMBER 20, 2022

This is due to an improvement in plant uptime, supported by predictive maintenance applications, coupled with optimization of the fuel consumption that powers these plants, as well as fine-grained performance monitoring that eliminates over-production. .

Energy 83

Energy Analytics Data Machine Learning 83

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 Machine Learning 98

KitelyTech

FEBRUARY 3, 2021

Database management is what your database administrator uses to store, organize, and access computer data. It often includes three main components like the database schema, database application, and data engine. Database management systems must be flexible enough to handle large amounts of data in a short period of time.

Big Data 52

Big Data Data Development Business Intelligence 52

Netflix Tech

NOVEMBER 22, 2019

December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Netflix Tech

NOVEMBER 22, 2019

December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Hu's Place - HitachiVantara

MAY 10, 2019

For this reason, many financial institutions are converting their fraud detection systems to machine learning and advanced analytics and letting the data detect fraudulent activity. This will require another product for data governance. Data Preparation : Data integrationthat is intuitive and powerful.

Data 90

Data Machine Learning Artificial Inteligence Weak Development Team 90

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 84

Data Analytics Cloud Technical Review 84

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

MAY 27, 2020

The automation capabilities and predictions produced by ML have various applications. Depending on the organization needs and the field of ML application, there will be a bunch of scenarios how models can be built and applied. Triggering the model from the application client. Application client : sends data to the model server.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

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. This greatly increases data processing capabilities.

Big Data 75

Big Data Data Engineering Engineering Data 75

Existek

JANUARY 11, 2022

In 2010, they launched Windows Azure, the PaaS, positioning it as an alternative to Google App Engine and Amazon EC2. They provided a few services like computing, Azure Bob storage, SQL Azure, and Azure Service Bus. The new structure enabled the opportunity to meet such customer needs in computing as storage, networking, and services.

Azure 52

Azure AWS Cloud How To 52

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. Then we perform frequent batch ETL from application databases to a data warehouse. Classic ETL. Stateless and elastic.

Storage 40

Storage Big Data Google Cloud Analysis 40

Netflix Tech

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 81

Storage Data Resources Data Engineering 81

Hu's Place - HitachiVantara

NOVEMBER 11, 2018

In 2019, CIOs will have to optimize applications of the newest cloud technology in response to their requirements. They require highly modernized digital application and service environments running on true cloud platforms like Hitachi Enterprise Cloud.

Cloud 78

Cloud Google Cloud Azure AWS 78