Cloudera

FEBRUARY 14, 2022

When we introduced Cloudera Data Engineering (CDE) in the Public Cloud in 2020 it was a culmination of many years of working alongside companies as they deployed Apache Spark based ETL workloads at scale. It’s no longer driven by data volumes, but containerization, separation of storage and compute, and democratization of analytics.

Data Engineering 113

Data Engineering Engineering Data Storage 113

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

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 112

Data Engineering Engineering Data Technical Review 112

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 104

Data Engineering Engineering Data Tools 104

CIO

OCTOBER 9, 2024

The data preparation process should take place alongside a long-term strategy built around GenAI use cases, such as content creation, digital assistants, and code generation. Known as data engineering, this involves setting up a data lake or lakehouse, with their data integrated with GenAI models.

Strategy 215

Strategy Data Artificial Inteligence Storage 215

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. Feature engineering.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

Honeycomb

APRIL 23, 2025

download Model-specific cost drivers: the pillars model vs consolidated storage model (observability 2.0) All of the observability companies founded post-2020 have been built using a very different approach: a single consolidated storage engine, backed by a columnar store. and observability 2.0. understandably). moving forward.

Weak Development Team 106

Weak Development Team Metrics Storage Engineering 106

Netflix Tech

OCTOBER 28, 2021

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix.

Data Engineering 72

Data Engineering Engineering Data Software Engineering 72

AWS Machine Learning - AI

NOVEMBER 20, 2024

The solution combines data from an Amazon Aurora MySQL-Compatible Edition database and data stored in an Amazon Simple Storage Service (Amazon S3) bucket. Solution overview Amazon Q Business is a fully managed, generative AI-powered assistant that helps enterprises unlock the value of their data and knowledge.

Data 110

Data AWS Groups Knowledge Base 110

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

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

Netflix Tech

DECEMBER 21, 2020

At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 84

Storage Data Resources Data Engineering 84

Perficient

FEBRUARY 13, 2023

Introduction: We often end up creating a problem while working on data. So, here are few best practices for data engineering using snowflake: 1.Transform Using COPY and SNOWPIPE is the fastest and cheapest way to load data. In fact, this is another example of using the right tools.

Data Engineering 69

Data Engineering Engineering Data Storage 69

O'Reilly Media - Data

APRIL 3, 2018

Data Science and Machine Learning sessions will cover tools, techniques, and case studies. This year’s sessions on Data Engineering and Architecture showcases streaming and real-time applications, along with the data platforms used at several leading companies. Data platforms. Privacy and security.

Machine Learning 240

Machine Learning Artificial Inteligence Company Case Study 240

AWS Machine Learning - AI

MARCH 13, 2025

After the data is transcribed, MaestroQA uses technology they have developed in combination with AWS services such as Amazon Comprehend to run various types of analysis on the customer interaction data. For example, Can I speak to your manager? To start developing this product, MaestroQA first rolled out a product called AskAI.

Generative AI 110

Generative AI CTO Coach AWS Artificial Inteligence 110

Cloudera

JANUARY 12, 2018

I mentioned in an earlier blog titled, “Staffing your big data team, ” that data engineers are critical to a successful data journey. That said, most companies that are early in their journey lack a dedicated engineering group. Image 1: Data Engineering Skillsets.

Data Engineering 54

Data Engineering Big Data Engineering Data 54

Erik Bernhardsson

DECEMBER 15, 2020

I had my first job as a software engineer in 1999, and in the last two decades I've seen software engineering changing in ways that have made us orders of magnitude more productive. These are just examples — I could go on all day. You need storage to build something to serve 1M concurrent users?

Software Engineering 325

Software Engineering Engineering Tools Software 325

Gorilla Logic

APRIL 27, 2022

Are you a data engineer or seeking to become one? This is the first entry of a series of articles about skills you’ll need in your everyday life as a data engineer. This blog post is for you. So let’s begin with the first and, in my opinion, the most useful tool in your technical tool belt, SQL.

Data Engineering 59

Data Engineering Engineering Data Windows 59

TechCrunch

MAY 18, 2022

When asked, Heartex says that it doesn’t collect any customer data and open sources the core of its labeling platform for inspection. “We’ve built a data architecture that keeps data private on the customer’s storage, separating the data plane and control plane,” Malyuk added.

Open Source 203

Open Source Weak Development Team Data Artificial Inteligence 203

AWS Machine Learning - AI

APRIL 23, 2025

Multiple specialized Amazon Simple Storage Service Buckets (Amazon S3 Bucket) store different types of outputs. Solution Components Storage architecture The application uses a multi-bucket Amazon S3 storage architecture designed for clarity, efficient processing tracking, and clear separation of document processing stages.

Artificial Inteligence 66

Artificial Inteligence Open Source AWS Serverless 66

TechCrunch

APRIL 12, 2022

While companies find AI’s predictive power alluring, particularly on the data analytics side of the organization, achieving meaningful results with AI often proves to be a challenge. It’s true that AI can help to project revenue, for example, by identifying trends in buying and selling.

Artificial Inteligence 202

Artificial Inteligence Machine Learning Open Source Cloud 202

CIO

FEBRUARY 13, 2023

For example, New York-Presbyterian Hospital, which has a network of hospitals and about 2,600 beds, is deploying over 150 AI and VR/AR projects this year across all clinical specialties. “We On-prem infrastructure will grow cold — with the exception of storage, Nardecchia says.

Budget 218

Budget Artificial Inteligence Technical Review VR 218

Trigent

JUNE 20, 2022

To do this, they are constantly looking to partner with experts who can guide them on what to do with that data. This is where data engineering services providers come into play. Data engineering consulting is an inclusive term that encompasses multiple processes and business functions.

Data Engineering 52

Data Engineering Engineering Data Strategy 52

AWS Machine Learning - AI

NOVEMBER 15, 2024

Generative AI models (for example, Amazon Titan) hosted on Amazon Bedrock were used for query disambiguation and semantic matching for answer lookups and responses. The first data source connected was an Amazon Simple Storage Service (Amazon S3) bucket, where a 100-page RFP manual was uploaded for natural language querying by users.

Generative AI 113

Generative AI AWS Groups Artificial Inteligence 113

Honeycomb

APRIL 21, 2025

The idea that telemetry data needs to be managed, or needs a strategy, draws a lot of inspiration from the data world (as in, BI and Data Engineering). Your company most likely has a data team that manages the data warehouse(s), data pipelines, data sources, and reporting tools.

Strategy 52

Strategy Data Technical Review Software Review 52

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. For example, how might social media spending affect sales? Data analytics examples.

Analytics 203

Analytics Data Analysis Business Analytics 203

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 174

Artificial Inteligence Strategy Data Machine Learning 174

Xebia

JANUARY 30, 2025

Let’s take an example Say your data is divided in two categories: personal and non-personal data. And that some people in your company should be allowed to view that personal data, while others should not. In our example we want some people (in the group can_handle_personal_data ) to be able to use the entire table.

Data 130

Data Groups Data Engineering Systems Review 130

CIO

JUNE 19, 2024

Currently, the USPTO’s platform engineering team is actively testing an AI capability that can detect performance constraints and address them by allocating more storage, for example, or adding more CPU or memory resources, or moving data from one repository to another. “AI Scale up, then expand out.

Weak Development Team 208

Weak Development Team Engineering UI/UX Software Development 208

CIO

JULY 15, 2022

The forecasting systems DTN had acquired were developed by different companies, on different technology stacks, with different storage, alerting systems, and visualization layers. Working with his new colleagues, he quickly identified rebuilding those five systems around a single forecast engine as a top priority.

Systems Review 167

Systems Review Fractional CTO System Development Team Review 167

O'Reilly Media - Data

JULY 18, 2018

For specific data types (like images), there are new companies like Neuromation, DataGen, and AI.Reverie, that can help lower the cost of training data through tools for generating synthetic data. Another way we can glean the value of data is to look at the valuation of startups that are known mainly for their data sets.

Machine Learning 196

Machine Learning Artificial Inteligence Data Marketing 196

O'Reilly Media - Ideas

JANUARY 8, 2019

The demand for data skills (“the sexiest job of the 21st century”) hasn’t dissipated. LinkedIn recently found that demand for data scientists in the US is “off the charts,” and our survey indicated that the demand for data scientists and data engineers is strong not just in the US but globally.

Trends 109

Trends Data Machine Learning Artificial Inteligence 109

Xebia

JANUARY 22, 2025

Many low cardinality columns are repeated for all rows, for example, type of car, brand, and ‘Taxi indicator’ to name a few. A columnar storage format like parquet or DuckDB internal format would be more efficient to store this dataset. See substring(<bitint>, example above. parquet # 1.2G

Analytics 130

Analytics Storage Engineering Comparison 130

AWS Machine Learning - AI

JULY 15, 2024

Data intake A user uploads photos into Mixbook. The raw photos are stored in Amazon Simple Storage Service (Amazon S3). The data intake process involves three macro components: Amazon Aurora MySQL-Compatible Edition , Amazon S3, and AWS Fargate for Amazon ECS. DJ Charles is the CTO at Mixbook.

Generative AI 120

Generative AI Artificial Inteligence AWS Technical Review 120

Cloudera

MAY 18, 2021

Second, since IaaS deployments replicated the on-premises HDFS storage model, they resulted in the same data replication overhead in the cloud (typical 3x), something that could have mostly been avoided by leveraging modern object store. Storage costs. using list pricing of $0.72/hour hour using a r5d.4xlarge

Cloud 86

Cloud Technical Review Storage Backup 86

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. Data scientists love Python, period.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

Altexsoft

APRIL 16, 2021

Despite the variety and complexity of data stored in the corporate environment, everything is typically recorded in simple columns and rows. This is a classic spreadsheet look we’re all familiar with, and that’s how most databases file data. An example of database tables, structuring music by artists, albums, and ratings dimensions.

Analytics 98

Analytics Analysis Storage Business Intelligence 98