Data Engineering Podcast

JULY 16, 2021

Summary There is a wealth of tools and systems available for processing data, but the user experience of integrating them and building workflows is still lacking. Raj Bains founded Prophecy to address this need by creating a UI first platform for building and executing data engineering workflows that orchestrates Airflow and Spark.

High Quality Data 100

High Quality Data Data Engineering Data Engineer Coding 100

Data Engineering Podcast

APRIL 14, 2024

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.

Non-relational Database 147

Non-relational Database Relational Database Database Designing 147

Data Engineering Podcast

MARCH 10, 2024

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.

Data Lake 147

Data Lake High Quality Data Data Pipeline Architecture 147

ProjectPro

FEBRUARY 21, 2023

Although challenging, a career in data engineering can be rewarding. Data engineers and their skills play a crucial role in the success of an organization by making it easier for data scientists , data analysts , and decision-makers to access the data they need to do their jobs.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Data Engineering Podcast

NOVEMBER 19, 2023

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.

Project 147

Project Data Lake High Quality Data SQL 147

Monte Carlo

AUGUST 17, 2021

Their artificial intelligence data-driven platform relies on high-quality data to make coverage recommendations for customers. In 2019, the company suffered from unreliable data and strained relationships between teams because stakeholders weren’t able to access the most up-to-date data they needed to make decisions.

Insurance 40

Insurance Retail Data Pipeline Portfolio 40

AltexSoft

FEBRUARY 21, 2023

The key objective behind this task is to set the best booking prices to entice customers and ensure that hotels take full advantage of their business potential. Our team faced the challenge of data scarcity when working on the Rakuten project. While we were engaged in our project, we had to filter out non-informative bookings.

Hospitality 52

Hospitality Datasets Algorithm Machine Learning 52

Monte Carlo

MAY 24, 2022

Monte Carlo research shows a correlation between data incidents and the amount of data an organization handles, with the average business experiencing at least one data incident for every 15 tables in their environment. “As The first several chapters of the book are accessible for free, here.

High Quality Data 52

High Quality Data Data Education Machine Learning 52

Monte Carlo

APRIL 21, 2022

By evaluating and documenting the state of your data within Snowflake, you can identify data quality issues such as stale data, data that fall outside of expected distribution ranges, incomplete tables, and schema changes. Other helpful governance features include Dynamic Data Masking and Row Access Policies.

Metadata 52

Metadata Bytes Government Data 52

Knowledge Hut

JANUARY 25, 2024

Standardizing Capitalization: The Language of Consistency Consider your data like a library, and inconsistent capitalization as books scattered randomly. Establish a style guide for your data, specifying how text should be capitalized, and ensure everyone follows these rules consistently.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Monte Carlo

AUGUST 10, 2023

Lineage : When data breaks, the first question is always “where?” 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. The original article that launched a category: What is data observability?

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

AltexSoft

AUGUST 22, 2022

So, instead of replacing or rebuilding the existing infrastructure, you add a new, ML-powered abstraction layer on top of the underlying data sources, enabling various users to access and manage the information they need without duplication. Data fabric architecture example. Unified data access.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Towards Data Science

MARCH 29, 2023

If you’re interested in those early days, how I grew my career, and advice for newcomers to data, take a look at my earlier article. In this article, I want to focus on my on-again, off-again relationship with books and reading. Even if you haven’t read any of the books below, you’ve probably at least heard of some of them.

Data Warehouse 68

Data Warehouse BI Healthcare Database 68

Monte Carlo

MARCH 28, 2024

A data product, coined by DJ Patil, Former Chief Data Scientist of the United States, has several components, including a product management process, the domain wrapper comprising a semantic layer, business logic and metrics, and access. Simply put, a data product is much more than a dataset alone. Who needs this data?

Data 52

Data High Quality Data Machine Learning Accessible 52

Precisely

JANUARY 11, 2023

To remain competitive, you must proactively and systematically pursue new ways to leverage data to your advantage. As the value of data reaches new highs, the fundamental rules that govern data-driven decision-making haven’t changed. To make good decisions, you need high-quality data.

Data Governance 52

Data Governance Government Metadata Data 52

Monte Carlo

MARCH 23, 2022

Their primary use case for data observability was so they could make bold, aggressive marketing investments with confidence in order to hit an ambitious 40% growth target. Some of our customers that get the most topline benefit from our data observability platform are companies where data is the product or at least a core component of it.

IT 52

IT Machine Learning High Quality Data Data 52

Monte Carlo

APRIL 26, 2022

Similarly, the discipline of DataOps helps teams remove silos and work more efficiently to deliver high-quality data products across the organization. DataOps professionals also leverage observability to decrease downtime as companies begint o ingest large amounts of data from various sources.

IT 64

IT Data Pipeline Data Engineering Data Engineer 64

Monte Carlo

JUNE 23, 2022

But those are specialized use cases, and while the demand for streaming data poses new big data challenges on the horizon, today most organizations enjoy a technological moment in time where they can cost effectively access enough storage and compute to meet their organization’s needs without breaking a sweat.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40

Monte Carlo

APRIL 1, 2021

Over the past few decades, the world of data analytics has undergone transformation from a siloed entity to a cross-functional powerhouse. Now, in 2021, in this decade of data, the time is ripe for yet another sea-change, this time in the form of data democratization and accessibility. What was your favorite book in 2020?

Building 52

Building BI Business Intelligence Data 52

Monte Carlo

MAY 17, 2023

For example, a one person data team at an insurance company found they were spending more time maintaining tools than actually using them to deliver data. With these bottlenecks and a lack of accessibility to—and therefore trust in—the data, many data consumers found workarounds by simply querying the source data directly.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

For example, a one person data team at an insurance company found they were spending more time maintaining tools than actually using them to deliver data. With these bottlenecks and a lack of accessibility to—and therefore trust in—the data, many data consumers found workarounds by simply querying the source data directly.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52