DataKitchen

SEPTEMBER 20, 2021

Data organizations don’t always have the budget or schedule required for DataOps when conceived as a top-to-bottom, enterprise-wide transformational change. An essential part of the DataOps methodology is Agile Development , which breaks development into incremental steps. In short, Lean DataOps is the fastest path to DataOps value.

Data Pipeline 98

Data Pipeline Data Cleanse Process Architecture 98

Monte Carlo

JANUARY 23, 2024

In short, Maxime argues that to effectively scale data science and analytics in the future, data teams needed a specialized engineer to manage ETL, build pipelines, and scale data infrastructure. Enter, the data engineer. As a result, being a data engineer was both a blessing and a curse. On the flipside, costs are more constrained. “It

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98

DataKitchen

OCTOBER 20, 2023

In today’s data-driven landscape, Data and Analytics Teams i ncreasingly face a unique set of challenges presented by Demanding Data Consumers who require a personalized level of Data Observability. Data Observability platforms often need to deliver this level of customization.

Insurance 70

Insurance Pharmaceutical Data Data Ingestion 70

Knowledge Hut

JANUARY 18, 2024

From social media posts and online transactions to sensor readings and healthcare records, data is the fuel that powers modern businesses and organizations. The data analytics future is brimming with exciting possibilities. So, get ready to dive into the captivating world of data analytics, where the future holds endless opportunities.

Data Analytics 52

Data Analytics Healthcare Machine Learning Algorithm 52

Databand.ai

AUGUST 30, 2023

DataOps Architecture: 5 Key Components and How to Get Started Ryan Yackel August 30, 2023 What Is DataOps Architecture? DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. As a result, they can be slow, inefficient, and prone to errors.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Data Engineering Podcast

OCTOBER 23, 2022

Summary Agile methodologies have been adopted by a majority of teams for building software applications. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. And don’t forget to thank them for their continued support of this show!

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Project Metadata MongoDB MySQL 130

Data Engineering Podcast

JUNE 26, 2022

Summary The proliferation of sensors and GPS devices has dramatically increased the number of applications for spatial data, and the need for scalable geospatial analytics. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Atlan is the metadata hub for your data ecosystem.

Datasets 130

Datasets Unstructured Data Metadata MongoDB 130

DataKitchen

DECEMBER 9, 2022

Query> DataOps. ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes.

Data Preparation 52

Data Preparation Machine Learning Government Data Analytics 52

DataKitchen

APRIL 28, 2021

Industry analysts who follow the data and analytics industry tell DataKitchen that they are receiving inquiries about “data fabrics” from enterprise clients on a near-daily basis. Forrester relates that out of 25,000 reports published by the firm last year, the report on data fabrics and DataOps ranked in the top ten for downloads in 2020.

Data Pipeline 98

Data Pipeline Data Data Analytics Architecture 98

Databand.ai

JANUARY 24, 2023

What is DataOps? The Ultimate Guide for Data Teams Eric Jones 2023-01-24 12:21:38 If you find yourself hearing a lot about DataOps and then subsequently asking, “ What is DataOps? This guide details everything you need to know about DataOps, including: What is DataOps? Why is DataOps important?

Retail 52

Retail Banking Government Manufacturing 52

Databand.ai

AUGUST 30, 2023

Unified DataOps: Components, Challenges, and How to Get Started Joseph Arnold August 30, 2023 What Is Unified DataOps? Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

Data Engineering Podcast

SEPTEMBER 8, 2021

For businesses that are working in the data management and analytics space they recognized the efforts of Timbr.ai, Soda Data, Nexla, and Tada. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. What will simplify this process, if any?

SQL 100

SQL Data Governance Database Data Engineering 100

DataKitchen

FEBRUARY 21, 2023

By Robert Thanaraj , Ehtisham Zaidi , and 2 more. Gartner suggests in the article that successful Data Engineering teams have two crucial challenges. How to optimize Data Team Productivity – essentially that teams should avoid adding more bodies whenever they have more work that needs to be done.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

DataKitchen

NOVEMBER 28, 2022

This is the final post in DataKitchen’s four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Process 52

DataKitchen

DECEMBER 6, 2022

We are excited that Gartner released its ‘Market Guide to DataOps’ ! The two things we are most excited about are: First, DataOps is distinct from all Data Analytic tools. How is DevOps different than DataOps ? DataOps Observability. We see teams do amazing things with our software.

Data Lake 52

Data Lake Hadoop Government Data Analytics 52

DataKitchen

NOVEMBER 18, 2022

This is the third post in DataKitchen’s four-part series on DataOps Observability. Observability is a methodology for providing visibility of every journey that data takes from source to customer value across every tool, environment, data store, team, and customer so that problems are detected and addressed immediately.

Datasets 52

Datasets Data Lake Data Pipeline Database 52

Monte Carlo

APRIL 26, 2022

What is DataOps? DataOps is a discipline that merges data engineering and data science teams to support an organization’s data needs, in a similar way to how DevOps helped scale software engineering. In this guide, we’ll explain each of the following: What is DataOps?

IT 64

IT Data Pipeline Data Engineering Data Engineer 64

Monte Carlo

SEPTEMBER 12, 2023

We had to know how their data team made it happen, especially in such a short span of time, so we recently sat down with Engineering Director Emmanuel Fuentes to learn more. Emmanuel leads the Machine Learning & Data Platforms teams at Whatnot, while his colleague Evan Hou heads up analytics and data science.

Machine Learning 52

Machine Learning Data Entertainment SQL 52

DataKitchen

MARCH 16, 2023

How to measure your data analytics team? So it’s Monday, and you lead a data analytics team of perhaps 30 people. But wait, she asks you for your team metrics. Like most leaders of data analytic teams, you have been doing very little to quantify your team’s success. Introduction.

Data Analytics 52

Data Analytics Manufacturing Data Process 52

Monte Carlo

MARCH 28, 2024

However, when your data quality alerts aren’t optimized, it’s easy for a data team to go from feeling like the captains of their data ship to feeling like they’re drowning in a sea of low-impact Slack pings. you had to know how different teams wanted to be alerted and tagging structures could get complex,” said Sean.

Data 52

Data Data Engineering Data Engineer Engineering 52

Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. Your CTO is excited about migrating to the cloud, and constantly sends your team articles highlighting performance measurements against some of the latest technologies.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Monte Carlo

SEPTEMBER 13, 2023

Our teams work hard to provide great experiences and improve real business outcomes, so we’re thrilled that our customers’ reviews earned us 14 Fall 2023 Awards , including Easiest to Do Business With, Most Implementable, and Best Estimated ROI.

AWS 52

AWS Education Database Data Pipeline 52

Monte Carlo

AUGUST 10, 2023

This approach results in healthier data pipelines, increased team productivity, enhanced data management practices, and ultimately, higher customer satisfaction. Data lineage provides the answer by telling you which upstream sources and downstream ingestors were impacted, as well as which teams are generating the data and who is accessing it.

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

Towards Data Science

JULY 24, 2023

The average unoptimized SQL query executed as part of an ad hoc analytics request will typically run a lot faster in these platforms that have been tuned to work well out of the box. Data platforms with combined but decoupled storage and compute also provide a more robust, integrated user experience across several key dataops tasks.

Metadata 96

Metadata Data Warehouse Data Lake Data Science 96

DataKitchen

MAY 10, 2021

As organizations strive to become more data-driven, Forrester recommends 5 actions to take to move from one stage of insights-driven business maturity to another. . Intermediates: Build on your successes and work to scale your IDB capabilities across the enterprise using agile and adaptive DevOps, DataOps, and ModelOps processes. .

Data Architecture 52

Data Architecture Machine Learning Data Science Government 52

Cloudera

SEPTEMBER 15, 2022

The gathering in 2022 marked the sixteenth year for top data and analytics professionals to come to the MIT campus to explore current and future trends. I spoke to Mark Ramsey of Ramsey International to unpack what this all means and how it can help teams create an architecture that delivers business use cases faster.

Data Lake 79

Data Lake Analytics Application Cloud Storage Architecture 79

DataKitchen

JANUARY 2, 2021

This is similar to findings in a joint Eckerson-DataKitchen DataOps survey. In this report, Gartner outlines recommendations to effectively operationalize AI solutions that involve the core management competencies of ModelOps, DataOps, and DevOps. Figure 1: Operational AI Requires ModelOps, DataOps, and DevOps Practices.

Data Engineering 52

Data Engineering Data Engineer Engineering Government 52

Data Engineering Podcast

DECEMBER 7, 2020

Different organizations have tried a multitude of organizational strategies to improve the success rate of these data teams with varying levels of success. Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud. What are the critical elements of a successful data team?

Building 100

Building Consulting Big Data Data Engineering 100

Knowledge Hut

JUNE 15, 2023

Data is, without a doubt, the king of all business domains these days. Every business unit, including marketing , production, and finance, uses data to make significant decisions and carry out its operations. As per AIM (Analytics India Mag), the market is experiencing a huge projection in demand for data engineers.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. Journeys provide a context for understanding and observing complex elements across and down a data analytic system.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

François Nguyen

MARCH 7, 2021

” with 3 articles : this one about Data domains and Team Topologies, a second one devoted to the architecture and the technology and the last one about change management and the needed skills. Solving the right problem Let’s say that your starting point is having multiple data teams and multiple data platforms.

Government 130

Government Data Governance Data Metadata 130

Monte Carlo

MAY 31, 2022

The 1,000+ company helps small, medium and enterprise businesses scale with the impact of video. Vimeo’s data team operates on a massive scale of more than 85 billion events per month. It is important for the team to push data as near real-time as possible and ensure its reliability.

BI 52

BI Data Warehouse Unstructured Data Data Schemas 52

Data Engineering Podcast

NOVEMBER 23, 2020

Summary One of the oldest aphorisms about data is "garbage in, garbage out", which is why the current boom in data quality solutions is no surprise. Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. How does Bigeye help teams understand and manage their data quality?

Hadoop 100

Hadoop Data Pipeline BI Data 100

Mutt Data

JULY 16, 2023

About The Company Addi is a Colombian Buy Now - Pay Later technology company focused on credit and banking solutions. Challenge Addi had established a functioning analytical data platform, however, they aimed to enhance its capabilities and take it to the next level by implementing a Modern Data Stack. Want To Dive In Deeper?

Data Lake 40

Data Lake Banking Data Ingestion Data Pipeline 40

Mutt Data

JULY 16, 2023

About The Company Addi is a Colombian Buy Now - Pay Later technology company focused on credit and banking solutions. Challenge Addi had established a functioning analytical data platform, however, they aimed to enhance its capabilities and take it to the next level by implementing a Modern Data Stack. Want To Dive In Deeper?

Data Lake 40

Data Lake AWS Banking Data Ingestion 40

Ascend.io

MARCH 2, 2023

“Snowflake greatly reduced our DBA overhead and simplified our infrastructure,” said Terry in a recent interview with the Ascend team. We couldn’t get data to the business fast enough, and the data we did ship wasn’t trustworthy. The system suffered frequent outages and regularly lost months of data.

Data Pipeline 52

Data Pipeline Media Portfolio Data Ingestion 52

Ascend.io

MARCH 2, 2023

“Snowflake greatly reduced our DBA overhead and simplified our infrastructure,” said Terry in a recent interview with the Ascend team. “Snowflake greatly reduced our DBA overhead and simplified our infrastructure,” said Terry in a recent interview with the Ascend team.

Data Pipeline 52

Data Pipeline Media Portfolio Data Ingestion 52