Kentik

APRIL 4, 2023

When evaluating solutions, whether to internal problems or those of our customers, I like to keep the core metrics fairly simple: will this reduce costs, increase performance, or improve the network’s reliability? In this case, choosing to separate the storage traffic from the normal business traffic enhances both performance and reliability.

Network 104

Network Load Balancer Cloud Backup 104

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

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 78

Data Insurance Metrics eBook 78

Kentik

APRIL 12, 2023

DataOps strategies require a robust data infrastructure, including data warehouses, data lakes, caches, and other data storage and processing systems. 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

Mentormate

OCTOBER 31, 2023

Whether it’s increased predictive accuracy or a quantifiable reduction in readmissions, the metrics should resonate with the key concerns of your identified stakeholders, ensuring the project remains aligned with its core objectives. They’re an evolving set of needs and costs that can change as your project progresses.

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

Cloudera

JANUARY 15, 2021

Optimized read and write paths to cloud object stores (S3, Azure Data Lake Storage, etc) with local caching, allowing workloads to run directly against data in shared object stores without explicit loading to local storage. No local data loading step was required prior to query execution. Results Drill Down.

Performance 92

Performance Cloud Data Storage 92

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

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 65

Open Source Analytics Software Review Metrics 65

Erik Bernhardsson

DECEMBER 6, 2022

I'm deliberately vague about what exact role I mean here: take it to mean data engineers, data scientists, ML engineers, analytics engineers, and maybe more roles. But under the hood, the we use a content-addressed storage system. I will be posting a lot more about it! ↩︎

CTO Coach 242

CTO Coach Fractional CTO Software Engineering Serverless 242

Altexsoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Altexsoft

DECEMBER 9, 2020

Sometimes, a data or business analyst is employed to interpret available data, or a part-time data engineer is involved to manage the data architecture and customize the purchased software. At this stage, data is siloed, not accessible for most employees, and decisions are mostly not data-driven.

Analytics 102

Analytics Technical Review Technology Applications 102

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 81

Machine Learning Artificial Inteligence Weak Development Team Construction 81

Altexsoft

AUGUST 21, 2019

Data obsession is all the rage today, as all businesses struggle to get data. But, unlike oil, data itself costs nothing, unless you can make sense of it. Dedicated fields of knowledge like data engineering and data science became the gold miners bringing new methods to collect, process, and store data.

Development 44

Development Software Engineering Data Engineering Architecture 44

Altexsoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. Data storage and processing.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. What does the high-performance data project have to do with the real Franz Kafka’s heritage? process data in real time and run streaming analytics. How Apache Kafka streams relate to Franz Kafka’s books.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

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

MAY 27, 2020

But, in any case, the pipeline would provide data engineers with means of managing data for training, orchestrating models, and managing them on production. Getting additional data from feature store. This storage for features provides the model with a quick access to data that can’t be accessed from the client.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

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

JULY 19, 2022

As the picture above clearly shows, organizations have data producers and operational data on the left side and data consumers and analytical data on the right side. Data producers lack ownership over the information they generate which means they are not in charge of its quality. It works like this.

Architecture 52

Architecture Data Analytics Data Engineering 52

Datavail

MARCH 10, 2022

Data stewards – provide oversight of data sets to maintain data integrity and ensure implementation of policies from the committee and end-user compliance with the policies. Others – data modelers, data engineers, data architects, and data quality analysts also contribute to the DG process.

Government 40

Government Strategy Weak Development Team Company 40

Altexsoft

MARCH 2, 2023

Whether you’re just getting started with data governance or looking to refine your existing program, this article will provide you with valuable insights and practical tips to help you manage your data more effectively. What is data governance? Its goal is to define and control all data governance initiatives.

Government 52

Government Tools Data Weak Development Team 52

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

O'Reilly Media - Ideas

MARCH 31, 2020

In “ The AI Hierarchy of Needs ,” Monica Rogati argues that you can build an AI capability only after you’ve built a solid data infrastructure, including data collection, data storage, data pipelines, data preparation, and traditional analytics. If you can’t walk, you’re unlikely to run.

Product Management 139

Product Management Artificial Inteligence Machine Learning Software Review 139

Altexsoft

NOVEMBER 9, 2021

Digitization has already greatly improved this business aspect, facilitating contract creation and editing, introducing e-signatures, increasing security, tracking expiration dates, and enabling convenient search and storage functionalities. Procurement metrics and KPIs. Emergency purchase ratio is a reverse metric.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

SEPTEMBER 9, 2022

Metadata management is a set of activities, technologies, and policies that target metadata collection, storage, and organizing. It aims at making data assets understandable and discoverable for users. Metadata storage usually implies developing a specialized repository. Metadata storage. What is metadata management?

Tools 59

Tools Technical Review Software Review Systems Review 59

Cloudera

SEPTEMBER 17, 2020

With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.

Data Engineering 102

Data Engineering Engineering Data Tools 102

O'Reilly Media - Ideas

JANUARY 25, 2024

Just a few notes on methodology: This report is based on O’Reilly’s internal “Units Viewed” metric. The data used in this report covers January through November in 2022 and 2023. Data analysis and databases Data engineering was by far the most heavily used topic in this category; it showed a 3.6%

Trends 118

Trends Technical Review Technology Artificial Inteligence 118

Altexsoft

FEBRUARY 22, 2022

Integration with a fuel management system allows for sharing fuel consumption data. If you don’t have one, but still want to track fuel metrics, there’s an alternative. Integration with parts and/or service providers enables cross-company data exchange and can streamline your relationships with your business partners.

Software Review 52

Software Review Technical Review Software Technology 52

AWS Machine Learning - AI

JANUARY 26, 2024

Next, make sure that all data at rest is encrypted with AWS KMS, including considering the use of CMKs, and all data and models are versioned and backed up using Amazon Simple Storage Service (Amazon S3) versioning and applying object-level immutability with Amazon S3 Object Lock.

Artificial Inteligence 119

Artificial Inteligence Generative AI Security Applications 119

Netflix Tech

NOVEMBER 22, 2019

In this session, we discuss the technologies used to run a global streaming company, growing at scale, billions of metrics, benefits of chaos in production, and how culture affects your velocity and uptime. Technology advancements in content creation and consumption have also increased its data footprint.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Netflix Tech

NOVEMBER 22, 2019

In this session, we discuss the technologies used to run a global streaming company, growing at scale, billions of metrics, benefits of chaos in production, and how culture affects your velocity and uptime. Technology advancements in content creation and consumption have also increased its data footprint.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Altexsoft

AUGUST 19, 2022

Power BI Desktop is a free, downloadable app that’s included in all Office 365 Plans, so all you need to do is sign up, connect to data sources, and start creating your interactive, customizable reports using a drag-and-drop canvas and hundreds of data visuals. You get 10GB of cloud storage and can upload 1GB of data at a time.

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

Kentik

MAY 31, 2017

It’s high time to move away from this legacy paradigm to a unified, scalable, real-time solution built on the power of big data. Today’s siloed network management tools can be traced back to an earlier era, when design was constrained by the limited computing, memory, and storage capacity of appliances or single-server software deployments.

Network 40

Network Tools Big Data Metrics 40

Altexsoft

AUGUST 4, 2021

For example, a company has a data mart containing all the financial data. The company may wish to model an OLAP cube to summarize this data by different dimensions: by time, by product, or by city, to name a few. Watch our video about data engineering to learn more about how data gets from sources to BI tools.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

NOVEMBER 5, 2021

To avoid errors that may threaten patient safety, AHIMA introduced the data quality management model which covers. Application, or the reason for data collection, Collection, or the process of data gathering, Warehousing, or systems and activities related to data storage and archiving, and. Supported data formats.

Technical Review 59

Technical Review Technology Software Review Healthcare 59

Cloudera

MAY 22, 2018

With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. track model metrics, performance, and any model artifacts the user specifies.

Data 41

Data Load Balancer Machine Learning Artificial Inteligence 41

Altexsoft

OCTOBER 3, 2019

People analytics is the analysis of employee-related data using tools and metrics. Dashboard with key metrics on recruiting, workforce composition, diversity, wellbeing, business impact, and learning. Choose metrics and KPIs to monitor and predict. How are given metrics interconnected with each other? Commute time.

Analytics 16

Analytics Company Off-The-Shelf How To 16