Monte Carlo

APRIL 1, 2024

But, for data engineers, there’s something else that comes pretty close to the top of that list: clean data. Data cleaning is an essential step to ensure your data is safe from the adage “garbage in, garbage out.” Implement Routine Data Audits Build a data cleaning cadence into your data teams’ schedule.

High Quality Data 52

High Quality Data Datasets Data Data Pipeline 52

DataKitchen

APRIL 11, 2024

DataOps and Its Benefits: The concept of DataOps was introduced, drawing parallels with DevOps in terms of its emphasis on automation, continuous integration, and providing a high-quality data production pipeline. The goal is to reduce errors and operational overhead, allowing data teams to focus on delivering value.

Manufacturing 62

Manufacturing High Quality Data Data Pipeline Data 62

ProjectPro

FEBRUARY 21, 2023

With so many data engineering certifications available , choosing the right one can be a daunting task. There are over 133K data engineer job openings in the US, but how will you stand out in such a crowded job market? The answer is- by earning professional data engineering certifications! AWS or Azure?

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

JANUARY 3, 2024

But RAG development comes with a learning curve, even for your most talented data engineers. They need to know prompt engineering , vector databases and embedding vectors , data modeling, data orchestration , data pipelines and all for RAG. Data engineers. It’s 2024 now.

Technology 113

Technology Database Data Governance Data Engineering 113

Monte Carlo

JANUARY 16, 2024

Table of Contents Solve data silos starting at the people-level Keep data governance approachable Oliver Gomes’ data governance best practices Manage and promote the value of high-quality data How will Generative AI impact data quality at Fox? The complexity of a modern data pipeline.

Government 52

Government High Quality Data Data Governance Entertainment 52

Databand.ai

JULY 17, 2023

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently. Learn more in our detailed guide to DataOps tools (coming soon) What Is MLOps?

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Databand.ai

JULY 11, 2023

There are several reasons why organizations need a data quality platform to ensure the accuracy and reliability of their data. With a data quality platform in place, decision-makers can trust the data they use, reducing the risk of costly mistakes and missed opportunities.

Data Cleanse 52

Data Cleanse Telecommunication High Quality Data BI 52

Monte Carlo

AUGUST 31, 2023

As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Databand.ai

JULY 5, 2023

What Are Data Observability Tools? Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. Data observability tools allow teams to detect issues such as missing values, duplicate records, or inconsistent formats early on before they affect downstream processes.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

Data Engineering Podcast

APRIL 14, 2024

The default association with the term "database" is relational engines, but non-relational engines are also used quite widely. In this episode Oren Eini, CEO and creator of RavenDB, explores the nuances of relational vs. non-relational engines, and the strategies for designing a non-relational database.

Non-relational Database 147

Non-relational Database Relational Database Database Designing 147

Monte Carlo

OCTOBER 4, 2022

In November of 2020, O’Reilly Media first approached us with the idea to author Data Quality Fundamentals: A Practitioner’s Guide to Building More Trustworthy Data Pipelines. It was an inflection point for a fledgling company that had only just begun to establish the category of data observability.

High Quality Data 52

High Quality Data Data Pipeline Media Data 52

Pipeline Data Engineering

OCTOBER 15, 2021

Data engineering salon. News and interesting reads about the world of data. Re-evaluating Kafka: issues and alternatives for real-time Olivia Iannone Technical Writer at Estuary Kafka’s challenges have exhausted many an engineer on the path to successful data streaming. What if there was an easier way?

High Quality Data 52

High Quality Data Machine Learning Kafka Data Pipeline 52

Precisely

MARCH 14, 2023

Read Quality data you can depend on – today, tomorrow, and beyond For many years Precisely customers have ensured the accuracy of data across their organizations by leveraging our leading data solutions including Trillium Quality, Spectrum Quality, and Data360 DQ+. What does all this mean for your business?

Data Integration 52

Data Integration High Quality Data BI Data 52

Ascend.io

JANUARY 10, 2024

AI models are only as good as the data they consume, making continuous data readiness crucial. Here are the key processes that need to be in place to guarantee consistently high-quality data for AI models: Data Availability: Establish a process to regularly check on data availability. Actionable tip?

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Monte Carlo

MARCH 24, 2023

The key differences are that data integrity refers to having complete and consistent data, while data validity refers to correctness and real-world meaning – validity requires integrity but integrity alone does not guarantee validity. What is Data Integrity? How Do You Maintain Data Integrity?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Monte Carlo

JULY 5, 2023

while overlooking or failing to understand what it really takes to make their tools — and, ultimately, their data initiatives — successful. When it comes to driving impact with your data, you first need to understand and manage that data’s quality. They can really understand [what it means] when data is wrong.”

High Quality Data 52

High Quality Data Data Software Engineer Software Engineering 52

Monte Carlo

JUNE 16, 2023

Data quality has broad applications across industries, but its importance and degree of quality required is also contextual to the use case. For example, in marketing, high-quality data can help businesses better understand their customers, allowing them to create more targeted and effective campaigns.

Data Integration 52

Data Integration Hospitality Banking Healthcare 52

Monte Carlo

DECEMBER 19, 2022

Data quality testing is the process of validating that key characteristics of a dataset match what is anticipated prior to its consumption. According to Gartner , bad data costs organizations on average an estimated $12.9 Download our Data Quality Testing 101 eBook So, what is data quality testing anyway?

High Quality Data 52

High Quality Data Data Bytes SQL 52

Snowflake

JULY 27, 2023

After we had a successful trial period that checked all the boxes, we started our migration in autumn 2021 — together with moving all our data transformation management into the OSS version of dbt. Once the data is in the warehouse, we are leveraging Snowflake’s data warehousing features to handle it.

Data Architecture 52

Data Architecture Architecture Building Structured Data 52

Monte Carlo

JUNE 12, 2023

And, occasionally, an entirely new data platform to meet evolving business needs. That’s precisely where Vinny Gilcreest , the Senior Director of Engineering, Data and Analytics at Tenable, found himself within the last two years. The team built out manual tests to address data quality issues as they came up.

Kafka 52

Kafka Algorithm Data Pipeline High Quality Data 52

Monte Carlo

AUGUST 8, 2023

By applying rules and checks, data validation testing verifies the data meets predefined standards and business requirements to help prevent data quality issues and data downtime. From this perspective, the data validation process looks a lot like any other DataOps process.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

Data Engineering Weekly

NOVEMBER 11, 2022

I took a break from data engineering and focused on system monitoring and observability for some time. Little did I know I would be building data pipelines to measure system efficiency instead of measuring business efficiency. As the famous saying goes; Once a data engineer, always a data engineer.

Systems 40

Systems High Quality Data Consulting Metadata 40

Monte Carlo

AUGUST 21, 2023

The most common expression of this occurs when machine learning is a core part of the product (perhaps personalization or fraud detection) and the association with the product or engineering team is more important than the association with the core data team. You can’t have a Gen AI project without discoverable, high quality data.

Data Science 98

Data Science Machine Learning Media Data 98

Data Engineering Podcast

FEBRUARY 18, 2024

In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and scalability of data lakes. Data lakes are notoriously complex. Your first 30 days are free!

Data Lake 262

Data Lake High Quality Data Data Warehouse Google Cloud 262

Monte Carlo

MAY 24, 2022

Before launching Monte Carlo, I spoke with hundreds of data leaders who all struggled to deliver high quality data despite having their teams spend upwards of 30% of their time and millions of dollars per year on this issue. JetBlue accelerated their data quality processes to match the real-time needs of their business.

High Quality Data 52

High Quality Data Data Lake Media Data 52

Data Engineering Podcast

APRIL 21, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. Data lakes are notoriously complex. Go to dataengineeringpodcast.com/dagster today to get started.

Data Lake 182

Data Lake High Quality Data Data Pipeline Machine Learning 182

The Modern Data Company

DECEMBER 29, 2022

For example, using artificial intelligence and machine learning, banks can better protect customer identities across multiple channels while ensuring that sensitive customer data remains absolutely secure. Instead of a patchwork set of tools and multiple copies of data, users can customize dashboards for the insights they really need.

Banking 52

Banking Pipeline-centric Building Finance 52

Towards Data Science

APRIL 6, 2023

Principles, practices, and examples for ensuring high quality data flows Source: DreamStudio (generated by author) Nearly 100% of companies today rely on data to power business opportunities and 76% use data as an integral part of forming a business strategy. PagerDuty), and — if you have one — a data catalog (e.g.,

Unstructured Data 82

Unstructured Data Metadata Data Coding 82

Data Engineering Podcast

MAY 5, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. Data lakes are notoriously complex. Go to dataengineeringpodcast.com/dagster today to get started.

Building 147

Building Data Lake High Quality Data Machine Learning 147

Data Engineering Podcast

APRIL 28, 2024

In this episode he explains the data collection and preparation process, the collection of model types and sizes that work together to power the experience, and how to incorporate it into your workflow to act as a second brain. Data lakes are notoriously complex. Go to dataengineeringpodcast.com/dagster today to get started.

Building 147

Building Data Lake High Quality Data Machine Learning 147

Data Engineering Podcast

MARCH 24, 2024

In this episode Pete Hunt, CEO of Dagster labs, outlines these new capabilities, how they reduce the burden on data teams, and the increased collaboration that they enable across teams and business units. Data lakes are notoriously complex. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!

Data Lake 162

Data Lake High Quality Data Hadoop Data Pipeline 162

Data Engineering Podcast

MAY 12, 2024

Summary Building a data platform is a substrantial engineering endeavor. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is supported by Code Comments, an original podcast from Red Hat. Data lakes are notoriously complex.

Management 130

Management Data Lake High Quality Data Machine Learning 130

Monte Carlo

SEPTEMBER 22, 2022

Most employees have access to domain specific dashboards and will also engage the data team when their questions are deeper than a dashboard can answer. “We We have a sole data engineer and the rest of our team is pretty analytics focused. You can think of it as nine analytics folks and our singular hero on data engineering.

High Quality Data 52

High Quality Data Data Data Engineering Data Engineer 52

Data Engineering Podcast

NOVEMBER 12, 2023

Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Data lakes are notoriously complex.

Software Engineer 147

Software Engineer Software Engineering Engineering Data Lake 147

DataKitchen

NOVEMBER 4, 2021

The typical pharmaceutical organization faces many challenges which slow down the data team: Raw, barely integrated data sets require engineers to perform manual , repetitive, error-prone work to create analyst-ready data sets. It often takes months to progress from a data lake to the final delivery of insights.

Process 98

Process Data Process Pharmaceutical Data Lake 98

Data Engineering Podcast

FEBRUARY 4, 2024

RisingWave is a database engine that was created specifically for stream processing, with S3 as the storage layer. In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable.

SQL 173

SQL Data Lake High Quality Data Data Pipeline 173

Monte Carlo

AUGUST 17, 2021

And your entire data engineering team is all in on this “data as code” movement. The flip side of this data-driven coin: your stakeholders (CEO and CTO included) ping you nearly every other hour to ask you: “is my data up-to-date?” To achieve this data trust, they turned to Monte Carlo.

Insurance 40

Insurance Retail Data Pipeline Portfolio 40