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. I’m here.

Data Engineering 69

Data Engineering Engineering Data Storage 69

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. Data cleansing and enrichment processes need to combine, filter, aggregate, and select different sets to answer questions we have.

Data Engineering 59

Data Engineering Engineering Data Windows 59

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 69

Data Engineering Engineering Data Software Engineering 69

Trigent

JUNE 20, 2022

Businesses worldwide are realizing the importance of the considerable volume of data they possess and the need to extract value from it and incorporate it into enhancing Customer Experience or simplifying operational processes for improved results. This is where data engineering services providers come into play.

Data Engineering 52

Data Engineering Engineering Data Strategy 52

TechCrunch

OCTOBER 13, 2022

Data engineers have a big problem. Almost every team in their business needs access to analytics and other information that can be gleaned from their data warehouses, but only a few have technical backgrounds. The New York-based startup announced today that it has raised $7.6

Operating System 233

Operating System Technical Review Analytics Systems Review 233

CIO

JUNE 7, 2022

What is data analytics? 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. What are the four types of data analytics?

Analytics 334

Analytics Data Analysis Business Analytics 334

Xebia

DECEMBER 22, 2023

Therefore, we have selected three different views on JIRA data that have proven their value in different organizations. Please remember, that data, while powerful, is context-dependent and only shows a part of the truth. Example: Showing the statuses, the transitions between them with the average time between them.

Weak Development Team 244

Weak Development Team Data Trends Software Review 244

Netflix Tech

JUNE 1, 2021

Data Engineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. For example?—?clinical

Data Engineering 52

Data Engineering Engineering Data Culture 52

CIO

FEBRUARY 14, 2023

By George Trujillo, Principal Data Strategist, DataStax Increased operational efficiencies at airports. To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making.

Artificial Inteligence 286

Artificial Inteligence Strategy Data Machine Learning 286

TechCrunch

APRIL 6, 2022

Ascend.io , a company developing data automation products for enterprise customers, has raised $31 million in a Series B round led by Tiger Global with participation from Shasta Ventures and existing investor Accel, it announced today. Rather, it was the ability to scale the productivity of the people who work with data.

Data 230

Data Technical Cofounder Weak Development Team Data Engineering 230

Cloudera

OCTOBER 19, 2021

Airflow has been adopted by many Cloudera Data Platform (CDP) customers in the public cloud as the next generation orchestration service to setup and operationalize complex data pipelines. A simple example of this would be parameterizing SQL query within the CDW operator. Easing development friction.

Data Engineering 115

Data Engineering Engineering Data Virtualization 115

Cloudera

APRIL 30, 2021

Cloudera Data Engineering (CDE) is a cloud-native service purpose-built for enterprise data engineering teams. Here is an example showing a simple PySpark program querying an ACID table. cde spark submit pyspark-example-1.py. These scenarios are illustrated with the example below.

Data Engineering 61

Data Engineering Engineering Data Software Review 61

Cloudera

AUGUST 20, 2020

Data from the US Treasury website show which companies received PPP loans and how many jobs were retained. Analysis of this data presents three challenges. First, the size of the data is significant. The amount of time to pull, curate, transform, retrieve and report on that data is time intensive. Objective.

Data Engineering 61

Data Engineering Engineering Programming Data 61

CIO

AUGUST 2, 2023

A data and analytics capability cannot emerge from an IT or business strategy alone. With both technology and business organization deeply involved in the what, why, and how of data, companies need to create cross-functional data teams to get the most out of it. What are some examples of data solutions in each of those buckets?

Data 230

Data Architecture Government Strategy 230

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

CIO

JUNE 6, 2023

The shortage of data science skills continues to frustrate organizations in their quest to become more data driven. CIO.com’s 2023 State of the CIO research found that data science/analytics is one of the top three tech-related skills CIOs are trying to hire – and 22% said it’s one of the three most difficult to fill.

Data 130

Data Analytics Government Culture 130

TechCrunch

NOVEMBER 28, 2022

It’s only as good as the models and data used to train it, so there is a need for sourcing and ingesting ever-larger data troves. But annotating and manipulating that training data takes a lot of time and money, slowing down the work or overall effectiveness, and maybe both. V7’s specific USP is automation.

Training 240

Training Data Technical Review Artificial Inteligence 240

TechCrunch

MAY 18, 2022

Heartex, a startup that bills itself as an “open source” platform for data labeling, today announced that it landed $25 million in a Series A funding round led by Redpoint Ventures. We agreed that the only viable solution was to have internal teams with domain expertise be responsible for annotating and curating training data.

Open Source 203

Open Source Weak Development Team Artificial Inteligence Data 203

TechCrunch

SEPTEMBER 13, 2022

This is especially true for data, a universally necessary input for web3 companies, with crypto data firms like Messari reportedly fundraising amid a down market. Goldsky , a data infrastructure company for crypto startups, has raised $20 million in a seed round led by Felicis and Dragonfly Capital.

Company 185

Company Data Infrastructure Engineering 185

CIO

AUGUST 4, 2022

For example, a football team consisting of 11 quarterbacks would get crushed in a game against talented linemen, running backs and receivers. Interestingly, many companies do just that, creating a disconnect between data science teams and IT/DevOps when it comes to AI development. Great teams incorporate a variety of skill sets.

Artificial Intelligence 320

Artificial Intelligence Artificial Inteligence DevOps Sport 320

TechCrunch

OCTOBER 7, 2021

Increasingly, conversations about big data, machine learning and artificial intelligence are going hand-in-hand with conversations about privacy and data protection. They could see that the longer-term issue would be a growing need and priority for data privacy. “But now we are running into the bottleneck of the data. .

Artificial Inteligence 246

Artificial Inteligence Technical Review Engineering Data 246

Netflix Tech

NOVEMBER 14, 2023

By Abhinaya Shetty , Bharath Mummadisetty In the inaugural blog post of this series, we introduced you to the state of our pipelines before Psyberg and the challenges with incremental processing that led us to create the Psyberg framework within Netflix’s Membership and Finance data engineering team.

Data 90

Data Analysis Data Engineering Comparison 90

TechCrunch

JULY 12, 2022

Machine learning can provide companies with a competitive advantage by using the data they’re collecting — for example, purchasing patterns — to generate predictions that power revenue-generating products (e.g. At a high level, Tecton automates the process of building features using real-time data sources.

Artificial Inteligence 249

Artificial Inteligence Machine Learning Marketing Data Engineering 249

Xebia

MARCH 10, 2021

Many organisations have a new ambition to become a data-driven organisation. In essence, this means the organisation wants to make better business decisions based on insights provided by data [4]. Data itself is not able to advise a business for better decision-making. Data & Analytics as a separate business domain.

DevOps 130

DevOps Data Analytics Policies 130

Altexsoft

FEBRUARY 11, 2023

Data is now one of the most valuable assets for any kind of business. The 11th annual survey of Chief Data Officers (CDOs) and Chief Data and Analytics Officers reveals 82 percent of organizations are planning to increase their investments in data modernization in 2023. What is a data architect?

Data 87

Data Data Engineering Big Data Architecture 87

Cloudera

JUNE 2, 2021

Data pipelines are in high demand in today’s data-driven organizations. As critical elements in supplying trusted, curated, and usable data for end-to-end analytic and machine learning workflows, the role of data pipelines is becoming indispensable.

Data 89

Data Data Engineering Machine Learning Artificial Inteligence 89

Honeycomb

FEBRUARY 20, 2024

It was difficult in lots of different ways, but one thing I didn’t elaborate on in several blog posts was how lucky I was to have a coherent way to get the data I needed to make the feature useful for users. The other “half” of the equation is having the right data to feed into the model to handle the use cases your users expect.

Data 62

Data Artificial Inteligence Weak Development Team Examples 62

CIO

DECEMBER 11, 2023

You have to balance the potential benefits of generative AI with significant, important operational issues, such as ensuring patient data privacy and complying with regulatory requirements. Any and all activities that focus on improving patient data, processes and communications are prime for improvement.

Generative AI 189

Generative AI Artificial Inteligence Healthcare Artificial Intelligence 189

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

TechCrunch

FEBRUARY 7, 2022

Location data is absolutely critical to such strategies, enabling leading enterprises to not only mitigate challenges, but unlock previously unseen opportunities. Throughout the COVID-19 recovery era, location data is set to be a core ingredient for driving business intelligence and building sustainable consumer loyalty.

Business Intelligence 230

Business Intelligence AWS Sustainability Data Engineering 230

CIO

FEBRUARY 15, 2024

We took it seriously and said we need to have software, data, and AI capabilities,” says Nilles, who signed on to the CDIO role at the time. “We But to achieve Henkel’s digital vision, Nilles would need to attract data scientists, data engineers, and AI experts to an industry they might not otherwise have their eye on.

Artificial Inteligence 340

Artificial Inteligence B2C Machine Learning Artificial Intelligence 340

O'Reilly Media - Ideas

AUGUST 18, 2020

So you need to redesign your company’s data infrastructure. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows. The Two Cultures of Data Tooling. A little of both?

Machine Learning 143

Machine Learning Artificial Inteligence Data Data Engineering 143

Xebia

MAY 16, 2023

Data Engineers were tempted by the pressure of the moment to give up on testing all together. There was no need for generating your own data; just take a percentage of production data. In many cases, these tasks ended up on the shoulders of the Data Engineers themselves. Overly restrictive governance.

Testing 130

Testing How To Data Engineering Engineering 130

O'Reilly Media - Data

JULY 10, 2018

Data scientists, data engineers, AI and ML developers, and other data professionals need to live ethical values, not just talk about them. The hard thing about being an ethical data scientist isn’t understanding ethics. It’s doing good data science. We already have good standards for data ethics.

Data 173

Data Weak Development Team Software Review Culture 173

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 97

Data Virtualization Banking Data Engineering 97

Mobilunity

MAY 8, 2023

An Enterprise Data Lake is a large, centralized repository that stores structured and unstructured data at scale. It is designed to store vast amounts of data from a variety of sources. Moreover an Enterprise Data Lake makes it accessible to data scientists, analysts, and other users across the enterprise.

Engineering 52

Engineering Data Storage AWS 52

CIO

JANUARY 17, 2023

It shows in his reluctance to run his own servers but it’s perhaps most obvious in his attitude to data engineering, where he’s nearing the end of a five-year journey to automate or outsource much of the mundane maintenance work and focus internal resources on data analysis. It’s not a good use of our time either.”

Data Engineering 291

Data Engineering Engineering Data CTO Coach 291