TechCrunch

APRIL 26, 2021

Faculty , a VC-backed artificial intelligence startup, has won a tender to work with the NHS to make better predictions about its future requirements for patients, based on data drawn from how it handled the COVID-19 pandemic. We are, I believe, a really effective and scalable AI company, not just for the U.K. and in Europe, Asia.

Artificial Inteligence 231

Artificial Inteligence Artificial Intelligence Machine Learning Scalability 231

Cloudera

DECEMBER 21, 2020

In this blog we will take you through a persona-based data adventure, with short demos attached, to show you the A-Z data worker workflow expedited and made easier through self-service, seamless integration, and cloud-native technologies. In our data adventure we assume the following: . Company data exists in the data lake.

Data 98

Data Virtualization Banking Data Engineering 98

CIO

FEBRUARY 13, 2023

This includes spending on strengthening cybersecurity (35%), improving customer service (32%) and improving data analytics for real-time business intelligence and customer insight (30%). CIOs anticipate an increased focus on cybersecurity (70%), data analysis (55%), data privacy (55%), AI/machine learning (55%), and customer experience (53%).

Budget 363

Budget Artificial Inteligence Technical Review VR 363

TechCrunch

JULY 25, 2022

The software enables HR teams to digitize employee records, automate administrative tasks like employee onboarding and time-off management, and integrate employee data from different systems. The company was founded in 2021 by Brian Ip, a former Goldman Sachs executive, and data engineer YC Chan.

Recruiting 224

Recruiting Technical Review Software Review Systems Review 224

Cloudera

JUNE 7, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

Data 113

Data Architecture Analytics Government 113

Netflix Tech

MARCH 7, 2024

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

System 90

System Machine Learning Artificial Inteligence Training 90

Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

Cloudera

JANUARY 20, 2021

to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. One big use case is with sensor data. Corporations now use this type of data to notify consumers and employees in real-time. Training Data in HBase and HDFS.

Machine Learning 113

Machine Learning Artificial Inteligence Applications Data 113

Mobilunity

MAY 3, 2024

When it comes to financial technology, data engineers are the most important architects. As fintech continues to change the way standard financial services are done, the data engineer’s job becomes more and more important in shaping the future of the industry. Knowledge of Scala or R can also be advantageous.

Data Engineering 52

Data Engineering Fintech Engineering Data 52

Cloudera

DECEMBER 10, 2020

Why worry about costs with cloud-native data warehousing? Have you been burned by the unexpected costs of a cloud data warehouse? If not, before adopting a cloud data warehouse, consider the true costs of a cloud-native data warehouse. These costs impede the adoption of cloud-native data warehouses.

Data 98

Data Technical Review Storage Systems Review 98

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

Altexsoft

JUNE 29, 2021

For example, Netflix takes advantage of ML algorithms to personalize and recommend movies for clients, saving the tech giant billions. This article will focus on the role of a machine learning engineer, their skills and responsibilities, and how they contribute to an AI project’s success. Who does what in a data science team.

Artificial Inteligence 105

Artificial Inteligence Machine Learning Engineering Data Engineering 105

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. How does Cloudera support Day 2 operations?

Backup 85

Backup Cloud Architecture Resources 85

CIO

JULY 18, 2023

The CIO’s biggest hiring challenge is clear: “There is simply not enough talent to go around,” says Scott duFour, global CIO of business payments company Fleetcor, for whom positions in areas such as AI, cloud architecture, and data science remain the toughest to fill. S&P Global, for example, is entering its AI 2.0 The net result?

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Technical Advisors Artificial Inteligence Generative AI How To 237

KitelyTech

FEBRUARY 3, 2021

Big data and data science are important parts of a business opportunity. How companies handle big data and data science is changing so they are beginning to rely on the services of specialized companies. User Data Collection. User data collection is data about a user who is collected for market research purposes.

Big Data 52

Big Data Data Development Business Intelligence 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. What is a data lake?

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

AUGUST 3, 2022

For example, EMR plus Lake Formation makes a compromise by only providing column level security but not controlling row filtering. . Customers will now get the same consistent view of their data with the analytic processing engine of their choice without any compromises. . Fine grained access control (FGAC) with Spark.

Policies 90

Policies Machine Learning Artificial Inteligence Data Engineering 90

Cloudera

JANUARY 15, 2021

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. The results demonstrate superior price performance of Cloudera Data Warehouse on the full set of 99 queries from the TPC-DS benchmark. Overview of Cloudera Data Warehouse.

Performance 97

Performance Cloud Data Storage 97

Cloudera

OCTOBER 4, 2023

Fast moving data and real time analysis present us with some amazing opportunities. Every organization has some data that happens in real time, whether it is understanding what our users are doing on our websites or watching our systems and equipment as they perform mission critical tasks for us. Don’t blink — or you’ll miss it!

Analytics 90

Analytics Machine Learning Artificial Inteligence Telecommunications 90

High Scalability

JUNE 15, 2020

This is a guest post by Eunice Do , Data Engineer at TripleLift , a technology company leading the next generation of programmatic advertising. The system is the data pipeline at TripleLift. TripleLift is an adtech company, and like most companies in this industry, we deal with high volumes of data on a daily basis.

Part-Time VPE 82

Part-Time VPE Data Advertising Data Engineering 82

Abhishek Tiwari

JULY 12, 2018

Building data pipelines can offer strategic advantages to the business. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines. Data pipeline initiatives are generally unfinished projects. In this post, we will discuss why you should avoid building data pipelines in first place.

Data 63

Data Software Review Technical Review Microservices 63

Mobilunity

DECEMBER 21, 2022

One of the essential goals of sourcing and using data is to help businesses make better decisions, so they can be at the top of their industries. To do this effectively, many businesses employ data scientists. These experts help to collect, visualize, and make sense of all kinds of data correctly. Agriculture.

Data 52

Data How To Machine Learning Artificial Inteligence 52

Perficient

JANUARY 10, 2024

In Spark with Scala, creating DataFrames is fundamental for data manipulation and analysis. You can create DataFrames from various data sources like CSV, JSON, or even from existing RDDs (Resilient Distributed Datasets). In this blog we will see some approaches towards creating dataframe with examples. schema” method.

Analysis 52

Analysis Data Engineering Data Scalability 52

Cloudera

SEPTEMBER 21, 2021

Many customers looking at modernizing their pipeline orchestration have turned to Apache Airflow, a flexible and scalable workflow manager for data engineers. But now it has become very simple and secure with our release of the Cloudera Airflow provider , which gives users the best of Airflow and CDP data services.

Off-The-Shelf 100

Off-The-Shelf Data Engineering Virtualization Cloud 100

Cloudera

MAY 13, 2021

but have you really examined the stream processing engines out there in a side-by-side comparison to make sure? Our Choose the Right Stream Processing Engine for Your Data Needs whitepaper makes those comparisons for you, so you can quickly and confidently determine which engine best meets your key business requirements.

Engineering 103

Engineering Comparison Open Source Scalability 103

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. Key challenges. Performance.

Big Data 63

Big Data Data Storage Microservices 63

TechCrunch

OCTOBER 7, 2021

If your customers are data engineers, it probably won’t make sense to discuss front-end web technologies. EveryDeveloper focuses on content, which I believe is the most scalable way to reach developers. The educational and inspirational content you use to attract developers will depend on who is the best fit for your product.

Weak Development Team 231

Weak Development Team Software Development Marketing Technical Advisors 231

AWS Machine Learning - AI

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

Artificial Inteligence 118

Artificial Inteligence Generative AI Security Applications 118

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

Kentik

SEPTEMBER 11, 2015

This how-to looks at using the Foreign Data Wrapper feature of PostgreSQL to enable access from your application to data that’s not in a PostgreSQL relational datastore. The how-to covers the following topics: About Foreign Data Wrappers. Example Implementation: WhiteDB. About Foreign Data Wrappers. Into the Code.

Data 40

Data Authentication Data Engineering Scalability 40

Altexsoft

APRIL 13, 2023

Leading executives focus on building resilient and intelligent supply chains that can withstand the turmoil due to data-based proactive decisions. But of course, you can only share data you have yourself – so higher visibility leads to higher transparency. Let’s look at what they say in recent surveys.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

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. However, they often forget about the fundamental work – data literacy, collection, and infrastructure – that must be done prior to building intelligent data products.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

CTOvision

JUNE 15, 2015

You know Spark, the free and open source complement to Apache Hadoop that gives enterprises better ability to field fast, unified applications that combine multiple workloads, including streaming over all your data. They also launched a plan to train over a million data scientists and data engineers on Spark.

Open Source 117

Open Source Big Data Machine Learning Artificial Inteligence 117

DataRobot

MARCH 10, 2022

These initiatives utilize interconnected devices and automated machines that create a hyperbolic increase in data volumes. This type of growth has stressed legacy data management systems and makes it nearly impossible to implement a profitable data-centered solution. High-level example of a common machine learning lifecycle.

Artificial Inteligence 52

Artificial Inteligence Artificial Intelligence Machine Learning IoT 52

TechCrunch

NOVEMBER 4, 2022

I know this because I used to be a data engineer and built extract-transform-load (ETL) data pipelines for this type of offer optimization. Part of my job involved unpacking encrypted data feeds, removing rows or columns that had missing data, and mapping the fields to our internal data models.

Data 218

Data Storage Analytics Data Engineering 218

Altexsoft

SEPTEMBER 9, 2022

Another example is labeling and categorizing objects in any scientific field, be it archeological finds, chemical samples, or living things in biology. Metadata is basically information that describes other data. It helps us understand the origin, structure, nature, and context of data. Metadata examples. date modified.

Tools 59

Tools Technical Review Software Review Systems Review 59

CIO

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

Analytics 324

Analytics Data Engineering Machine Learning Artificial Inteligence 324