CIO

FEBRUARY 14, 2023

Here are some examples: Fraud It’s critical to identify bad actors using high-quality AI models and data Product recommendations It’s important to stay competitive in today’s ever-expanding online ecosystem with excellent product recommendations and aggressive, responsive pricing against competitors.

Artificial Inteligence 291

Artificial Inteligence Strategy Data Machine Learning 291

O'Reilly Media - Data

APRIL 11, 2018

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.

Data Engineering 280

Data Engineering Engineering Data Artificial Inteligence 280

Netflix Tech

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Auto Remediation generates recommendations by considering both performance (i.e., Multi-objective optimizations.

Machine Learning 84

Machine Learning Artificial Inteligence Data Systems Review 84

Cloudera

JULY 18, 2023

In the realm of big data analytics, Hive has been a trusted companion for summarizing, querying, and analyzing huge and disparate datasets. But let’s face it, navigating the world of any SQL engine is a daunting task, and Hive is no exception. Can I set performance expectations with SLAs? How many queries failed?

Systems Review 73

Systems Review Metrics Trends Performance 73

Altexsoft

AUGUST 25, 2020

Big data can be quite a confusing concept to grasp. What to consider big data and what is not so big data? Big data is still data, of course. But it requires a different engineering approach and not just because of its amount. Data engineering vs big data engineering.

Big Data 75

Big Data Data Engineering Engineering Data 75

Altexsoft

JUNE 29, 2021

For example, Netflix takes advantage of ML algorithms to personalize and recommend movies for clients, saving the tech giant billions. MLEs are usually a part of a data science team which includes data engineers , data architects, data and business analysts, and data scientists.

Artificial Inteligence 105

Artificial Inteligence Machine Learning Engineering Data Engineering 105

Mobilunity

MAY 8, 2023

Processing zone is used for transforming and processing the raw data into a more usable format. This includes data cleansing, normalization, and enrichment. Analytics zone is where data analysts and data scientists can access the data to perform queries, generate reports, and create models.

Engineering 52

Engineering Data Storage AWS 52

CTOvision

JUNE 15, 2015

For years it has seemed like IBM was giving lip-service to Spark while emphasizing capabilities that they placed big bets on over the years (especially IBM's flagship stream processing product, InfoSphere Streams). Spark is still new and frankly there have been issues with performance from time to time.

Open Source 117

Open Source Big Data Machine Learning Artificial Inteligence 117

Kentik

DECEMBER 5, 2016

The ability of Kentik Detect to offer cloud-friendly network performance monitoring — Kentik NPM — was key to the recognition we earned. In a recent blog post by Kentik Solutions Engineer Eric Graham we explained how we “dog food” our own NPM solution to troubleshoot network performance issues within our own cloud-based application.

Network 40

Network Performance Metrics Software Review 40

O'Reilly Media - Ideas

APRIL 9, 2019

A few months ago, I wrote about the differences between data engineers and data scientists. An interesting thing happened: the data scientists started pushing back, arguing that they are, in fact, as skilled as data engineers at data engineering. Data engineering is not in the limelight.

Data Engineering 111

Data Engineering Engineering Data Technical Review 111

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. Data scientists love Python, period.

Machine Learning 106

Machine Learning Artificial Inteligence Scalability Data Engineering 106

Xebia

MARCH 10, 2021

One example to illustrate what often happens in practice: a data scientist from a business team creates an analytical model on their own laptop and everything works. Next, the data scientist asks an engineer in the Data & Analytics domain to ‘productionize’ the analytical model.

DevOps 130

DevOps Data Analytics Policies 130

Altexsoft

MARCH 16, 2021

In this article, we will explain the concept and usage of Big Data in the healthcare industry and talk about its sources, applications, and implementation challenges. What is Big Data and its sources in healthcare? So, what is Big Data, and what actually makes it Big? Let’s see where it can come from.

Big Data 116

Big Data Healthcare Applications Data 116

Cloudera

JUNE 18, 2022

As use cases matured, we saw the need for both efficient, interactive BI analytics and transactional semantics to modify data. The first generation of the Hive Metastore attempted to address the performance considerations to run SQL efficiently on a data lake. Iterations of the lakehouse.

Data 91

Data Analytics Open Source Architecture 91

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

CTOvision

MARCH 1, 2016

In addition, Hewlett Packard Enterprise and Hortonworks announced a joint collaboration to optimize enterprise Spark performance. Apache Hadoop Wordcount Example (examples.javacodegeeks.com). SanDisk: Senior Big Data Engineer/Hadoop Developer (kdnuggets.com). Watch Hortonworks ' Announcement Event here.

Analytics 61

Analytics Strategy Big Data Data Engineering 61

AWS Machine Learning - AI

JANUARY 26, 2024

Lastly, we connect these together with an example LLM workload to describe an approach towards architecting with defense-in-depth security across trust boundaries. We then discuss how building on a secure foundation is essential for generative AI.

Artificial Inteligence 117

Artificial Inteligence Generative AI Security Applications 117

Hacker Earth Developers Blog

JUNE 26, 2019

Also, the candidate should have knowledge of the different metrics used to evaluate the performance of a model. . The candidate should have a basic understanding of business or the industry in which he is applying as a data scientist. You could have a bunch of data and very little idea on what to do with it. Neural Networks .

Data 200

Data How To Recruiting Machine Learning 200

Kentik

MAY 23, 2017

Given the advanced capabilities provided by cloud and big data technology, there’s no longer any justification for legacy monitoring appliances that summarize away all the details and force operators to swivel between siloed tools. ISPs can gain similar advantages by becoming far more data driven.

Network 40

Network Open Source Big Data Load Balancer 40

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

Apiumhub

APRIL 1, 2021

Each of these concepts is explained well and there are examples along with an explanation of how the concepts are relevant in data science. Important note: It is a quick and easy reference, however, is not sufficient for mastering the concepts in-depth as the explanations and examples are not detailed.

Artificial Inteligence 52

Artificial Inteligence Data Handbook Machine Learning 52

Hu's Place - HitachiVantara

SEPTEMBER 26, 2018

The example here was the music industry where the business shifted from recordings to and streaming music. The music industry was panicked over the loss of revenue, but today the music industry is making more money than ever due to live performances which are promoted by their music. Puzzles can be solved by gathering more data.

Software Review 60

Software Review Systems Review Technical Review Big Data 60

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Data 106

Data Artificial Inteligence Analytics Machine Learning 106

O'Reilly Media - Ideas

JUNE 4, 2019

Ken Blanchard on Leading at a Higher Level: 4 Keys to Creating a High Performing Organization , June 13. Engineering Mentorship , June 24. Spotlight on Learning From Failure: Hiring Engineers with Jeff Potter , June 25. Performance Goals for Growth , July 31. Real-time Data Foundations: Spark , August 15.

Course 65

Course Training Artificial Inteligence Software Review 65

Altexsoft

APRIL 13, 2023

to see where freight is in real time and what the ETA is, share this information with customers, monitor and analyze fleet/carrier performance, and much more. Previously, control towers were seen as multi-faceted dashboards to monitor performance. Sometimes, they were designed to control a particular aspect of a supply chain.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Altexsoft

APRIL 22, 2020

So, along with data scientists who create algorithms, there are data engineers, the architects of data platforms. In this article we’ll explain what a data engineer is, the field of their responsibilities, skill sets, and general role description. What is a data engineer?

Data Engineering 69

Data Engineering Engineering Artificial Inteligence Data 69

CIO

JULY 14, 2023

To get good output, you need to create a data environment that can be consumed by the model,” he says. You need to have data engineering skills, and be able to recalibrate these models, so you probably need machine learning capabilities on your staff, and you need to be good at prompt engineering.

Generative AI 246

Generative AI Artificial Inteligence Open Source ChatGPT 246

Altexsoft

JUNE 11, 2021

In simple words, it’s about finding out how the processes are actually performed to discover problems and areas for improvement. The control-flow perspective is about the order of activities, and the goal here is to find the optimal path of performing a process. We’ll describe some real-world examples in one of the following sections.

Applications 69

Applications Weak Development Team Systems Review Software Review 69

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

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. Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

OpenCredo

JANUARY 26, 2023

The second step is to extract the data into a staging area (which can be sourced from a combination of multiple sources, but in our case, we simply have a single “big” dataset). As the last step (for this blogpost) we will load the data into a graph database where you can do further transformations and analysis as you like.

Big Data 52

Big Data Data Software Engineering Data Engineering 52

Altexsoft

JUNE 25, 2019

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. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

O'Reilly Media - Ideas

JUNE 5, 2019

Ken Blanchard on Leading at a Higher Level: 4 Keys to Creating a High Performing Organization , June 13. Engineering Mentorship , June 24. Spotlight on Learning From Failure: Hiring Engineers with Jeff Potter , June 25. Performance Goals for Growth , July 31. Real-time Data Foundations: Spark , August 15.

Course 49

Course Training Artificial Inteligence Software Review 49

Kentik

MAY 23, 2016

Let’s suppose, for example, that you’ve been going through network B to get traffic to and from network C, and you then discover that it would be possible to connect with network C directly. Improved Performance : Bypassing intervening networks (like network B) reduces the number of hops between the two networks.

Big Data 40

Big Data Analytics Internet Network 40

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. Different audience. Challenges.

Storage 40

Storage Big Data Google Cloud Analysis 40

Hacker Earth Developers Blog

JUNE 26, 2019

Also, the candidate should have knowledge of the different metrics used to evaluate the performance of a model. . The candidate should have a basic understanding of business or the industry in which he is applying as a data scientist. You could have a bunch of data and very little idea on what to do with it. Neural Networks .

Data 100

Data How To Recruiting Machine Learning 100

Altexsoft

JULY 29, 2022

Depending on how you measure it, the answer will be 11 million newspaper pages or… just one Hadoop cluster and one tech specialist who can move 4 terabytes of textual data to a new location in 24 hours. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics.

Big Data 59

Big Data Data Google Cloud Storage 59