TechCrunch

MAY 10, 2022

. “Typically, most companies are bottlenecked by data science resources, meaning product and analyst teams are blocked by a scarce and expensive resource. With Predibase, we’ve seen engineers and analysts build and operationalize models directly.” tech company, a large national bank and large U.S. healthcare company.”

Artificial Inteligence 235

Artificial Inteligence Machine Learning Off-The-Shelf Training 235

Capgemini

JANUARY 17, 2023

By creating a lakehouse, a company gives every employee the ability to access and employ data and artificial intelligence to make better business decisions. Many organizations that implement a lakehouse as their key data strategy are seeing lightning-speed data insights with horizontally scalable data-engineering pipelines.

Strategy 52

Strategy Technical Review Data Artificial Inteligence 52

Exadel

FEBRUARY 20, 2023

Vertex AI leverages a combination of data engineering, data science, and ML engineering workflows with a rich set of tools for collaborative teams. You can use the service to train algorithms, deploy models, and manage MLOps.

Artificial Inteligence 52

Artificial Inteligence Cloud Machine Learning Azure 52

Hu's Place - HitachiVantara

MAY 10, 2019

A Big Data Analytics pipeline– from ingestion of data to embedding analytics consists of three steps Data Engineering : The first step is flexible data on-boarding that accelerates time to value. This will require another product for data governance. This is colloquially called data wrangling.

Data 90

Data Machine Learning Artificial Inteligence Weak Development Team 90

Confluent

FEBRUARY 6, 2019

This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. 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

Altexsoft

AUGUST 25, 2021

Sentiment analysis results by Google Cloud Natural Language API. Open-source toolkits. In this article, we want to give an overview of popular open-source toolkits for people who want to go hands-on with NLP. Comparing popular open-source NLP tools. Spam detection. High level of expertise.

Tools 139

Tools Artificial Inteligence Technical Review Systems Review 139

Apiumhub

AUGUST 12, 2021

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. She started her own startup (Cubicus) in 2013. Twitter: [link] Linkedin: [link].

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Xebia

FEBRUARY 25, 2024

Data science is generally not operationalized Consider a data flow from a machine or process, all the way to an end-user. 2 In general, the flow of data from machine to the data engineer (1) is well operationalized. You could argue the same about the data engineering step (2) , although this differs per company.

Applications 130

Applications Technical Review Software Review Azure 130

Abhishek Tiwari

DECEMBER 22, 2017

As you can see data transformation before the load is an important and necessary step in this classic ETL model, and with ELT approach we are making data transformation more on-demand. This type of analysis is greatly eased by open source tools such RStudio, Jupyter, Zeppelin along with scripting languages R and Python.

Storage 40

Storage Big Data Google Cloud Analysis 40

Apiumhub

APRIL 18, 2019

Speakers come from all corners of the world to share their experience in various technologies and to invite everyone to participate in Open Source Technologies and in the JCP. Alex Soto – Java Champion, Engineer @ Red Hat. David Gageot – Developer Advocate at Google Cloud. 700 Java lovers. 10 workshops.

Technical Review 57

Technical Review Microservices Software Review CTO Coach 57

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

APRIL 19, 2021

The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). The need for people managing and maintaining computing infrastructure was comparatively low (24%), hinting that companies are solving their infrastructure requirements in the cloud.

Enterprise 143

Enterprise Survey Weak Development Team Education 143

TechCrunch

MARCH 22, 2021

Like similar startups, y42 extends the idea data warehouse, which was traditionally used for analytics, and helps businesses operationalize this data. At the core of the service is a lot of open source and the company, for example, contributes to GitLabs’ Meltano platform for building data pipelines.

Business Intelligence 244

Business Intelligence Software Review B2B Analytics 244

Kentik

APRIL 11, 2016

Developed as a model for “processing and generating large data sets,” MapReduce was built around the core idea of using a map function to process a key/value pair into a set of intermediate key/value pairs, and then a reduce function to merge all intermediate values associated with a given intermediate key.

Big Data 40

Big Data Analytics Network Architecture 40

CIO

AUGUST 18, 2022

You can intuitively query the data from the data lake. Users coming from a data warehouse environment shouldn’t care where the data resides,” says Angelo Slawik, data engineer at Moonfare. Rather than moving data into a central warehouse, the mesh enables access while allowing data to stay where it is.

Data 348

Data Artificial Inteligence Technical Advisors Technical Review 348

O'Reilly Media - Ideas

JANUARY 25, 2024

Our own theory is that it’s a reaction to GPT models leaking proprietary code and abusing open source licenses; that could cause programmers to be wary of public code repositories. This change is apparently not an error in the data. If you want to run an open source language model on your laptop, try llamafile.)

Trends 114

Trends Technical Review Technology Artificial Inteligence 114

Altexsoft

JULY 29, 2022

Apache Hadoop is an open-source Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. 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. How data engineering works under the hood.

Big Data 59

Big Data Data Google Cloud Storage 59

Xebia

JANUARY 9, 2024

Let’s imagine we are running dbt as a container within a cloud run job (a cloud-native container runtime within Google Cloud). Every morning when all the raw source data is ingested, we spin up a container via a trigger to do our daily data transformation workload using dbt.

Testing 130

Testing Data Open Source Google Cloud 130

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

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. Source: Apache Airflow.

Weak Development Team 52

Weak Development Team Technical Review Software Review Systems Review 52

Altexsoft

APRIL 16, 2021

An overview of data warehouse types. Optionally, you may study some basic terminology on data engineering or watch our short video on the topic: What is data engineering. What is data pipeline. Creating a cube is a custom process each time, because data can’t be updated once it was modeled in a cube.

Analytics 98

Analytics Analysis Storage Business Intelligence 98

O'Reilly Media - Ideas

MARCH 1, 2023

Data Data is another very broad category, encompassing everything from traditional business analytics to artificial intelligence. Data engineering was the dominant topic by far, growing 35% year over year. Data engineering deals with the problem of storing data at scale and delivering that data to applications.

Trends 132

Trends Technical Review Technology Software Review 132

Altexsoft

DECEMBER 4, 2020

Three types of data migration tools. Use cases: small projects, specific source and target locations not supported by other solutions. Automation scripts can be written by data engineers or ETL developers in charge of your migration project. Phases of the data migration process. Data sources and destinations.

Software Review 69

Software Review Software Data Technical Review 69

Daniel Bryant

SEPTEMBER 13, 2019

What was worth noting was that (anecdotally) even engineers from large organisations were not looking for full workload portability (i.e. There were also two patterns of adoption of HashiCorp tooling I observed from engineers that I chatted to: Infrastructure-driven?—?in

Infrastructure 8

Infrastructure Azure Software Engineering Cloud 8

Altexsoft

OCTOBER 21, 2022

Similar to Google in web browsing and Photoshop in image processing, it became a gold standard in data streaming, preferred by 70 percent of Fortune 500 companies. Apache Kafka is an open-source, distributed streaming platform for messaging, storing, processing, and integrating large data volumes in real time.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

APRIL 26, 2022

Initially built on top of the Amazon Web Services (AWS), Snowflake is also available on Google Cloud and Microsoft Azure. As such, it is considered cloud-agnostic. Modern data pipeline with Snowflake technology as its part. Source: Snowflake. BTW, we have an engaging video explaining how data engineering works.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Cloudera

DECEMBER 12, 2018

And so Impala was really about taking the experience of these big MPP systems on top of distributed file systems and moving that into an open source project for the world to use. As many of our customers already know, Apache Impala is one of the key components of our Modern Data Warehouse offering. Greg Rahn: Oh, definitely.

Data 42

Data Marketing Storage Big Data 42

Altexsoft

DECEMBER 15, 2021

The rest is done by data engineers, data scientists , machine learning engineers , and other high-trained (and high-paid) specialists. For better guidance, we’ve divided existing AutoML offerings into three large groups — tech giants, specific end-to-end AutoML platforms, and free open source libraries.

Machine Learning 59

Machine Learning Artificial Inteligence How To Azure 59

Altexsoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Weak Development Team 64

Weak Development Team Machine Learning Artificial Inteligence Software Review 64

O'Reilly Media - Ideas

JANUARY 25, 2022

A quick look at bigram usage (word pairs) doesn’t really distinguish between “data science,” “data engineering,” “data analysis,” and other terms; the most common word pair with “data” is “data governance,” followed by “data science.” It’s clear that Amazon Web Services’ competition is on the rise.

Trends 110

Trends Technical Review Technology Artificial Inteligence 110

UruIT

APRIL 27, 2020

Google Cloud . MathWork focused on the development of these tools to become experts in high-end financial use and data engineering contexts. Also, its solid presence in data science and machine learning software marketplace has built a strong user base. . H20.ai Pricing: free 2-week trial.

Artificial Inteligence 78

Artificial Inteligence Machine Learning Azure Software Review 78

UruIT

JULY 23, 2019

Having these requirements in mind and based on our own experience developing ML applications, we want to share with you 10 interesting platforms for developing and deploying smart apps: Google Cloud. MathWork focused on the development of these tools in order to become experts on high-end financial use and data engineering contexts.

Artificial Inteligence 81

Artificial Inteligence Machine Learning Azure Software Review 81

O'Reilly Media - Ideas

JANUARY 25, 2021

Terraform , HashiCorp’s open source tool for automating the configuration of cloud infrastructure, also shows strong (53%) growth. It’s more interesting to look at the story the data tells about the tools. An integrated solution from a cloud vendor (for example, Microsoft’s open source Dapr distributed runtime ).

Programming 145

Programming Cloud Artificial Inteligence Machine Learning 145