Data Engineering Podcast

FEBRUARY 4, 2024

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. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex.

SQL 173

SQL Data Lake High Quality Data Data Pipeline 173

Knowledge Hut

SEPTEMBER 25, 2023

This demonstrates how in-demand Microsoft Certified Data Engineers are becoming. They are moving their servers and on-premises data to Azure Cloud. What does all of this mean for Data Engineering professionals? Who is an Azure Data Engineer? Azure Data Engineers work with these and other solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Data Engineering Weekly

JANUARY 15, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Take control of your customer data today.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Knowledge Hut

NOVEMBER 2, 2023

Azure Data engineering projects are complicated and require careful planning and effective team participation for a successful completion. While many technologies are available to help data engineers streamline their workflows and guarantee that each aspect meets its objectives, ensuring that everything works properly takes time.

Data Engineering 52

Data Engineering Data Engineer Project Coding 52

Knowledge Hut

DECEMBER 28, 2023

Its comprehensive suite of services can handle data at scale. It’s no surprise that the demand for certified Azure data engineers has skyrocketed. Today, Azure Data Engineer certification is an invaluable asset for those looking to excel in the field of data engineering. Who is an Azure Data Engineer?

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Data Engineering Podcast

APRIL 3, 2022

Summary The flexibility of software oriented data workflows is useful for fulfilling complex requirements, but for simple and repetitious use cases it adds significant complexity. In this episode Satish Jayanthi explains how he is building a framework to allow enterprises to move quickly while maintaining guardrails for data workflows.

Data Warehouse 100

Data Warehouse Data Workflow Data Architecture SQL 100

Data Engineering Podcast

DECEMBER 18, 2022

In this episode Abe Gong brings his experiences with the Great Expectations project and community to discuss the technical and organizational considerations involved in implementing these constraints to your data workflows. Or sent out frustrating slack messages trying to find the right data set?

Metadata 130

Metadata Business Intelligence Data Lake BI 130

Hepta Analytics

FEBRUARY 14, 2022

This week, we got to think about our data ingestion design. We looked at the following: How do we ingest – ETL vs ELT Where do we store the data – Data lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Data Engineering Weekly

DECEMBER 29, 2022

Data Catalog as a passive web portal to display metadata requires significant rethinking to adopt modern data workflow, not just adding “modern” in its prefix. I know that is an expensive statement to make😊 To be fair, I’m a big fan of data catalogs, or metadata management , to be precise.

Metadata 62

Metadata Data Warehouse ETL Tools Data Workflow 62

Data Engineering Podcast

NOVEMBER 14, 2021

In this episode Michael Harper advocates for proactive data quality and starting with the source, rather than being reactive and having to work backwards from when a problem is found. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.

Data Warehouse 100

Data Warehouse BI Data Workflow Data 100

Data Engineering Podcast

FEBRUARY 20, 2022

In this episode Guy Yachdav, director of software engineering for ImmunAI, shares the complexities that are inherent to managing data workflows for bioinformatics. RudderStack’s smart customer data pipeline is warehouse-first. RudderStack’s smart customer data pipeline is warehouse-first.

Systems 100

Systems Software Engineer Software Engineering Data Warehouse 100

Data Engineering Podcast

JANUARY 1, 2022

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. Missing data?

Data Warehouse 100

Data Warehouse SQL Hadoop Data Lake 100

Data Engineering Podcast

OCTOBER 22, 2021

In this episode Oliver Laslett describes why dashboards aren’t sufficient for business analytics, how Lightdash promotes the work that you are already doing in your data warehouse modeling with dbt, and how they are focusing on bridging the divide between data teams and business teams and the requirements that they have for data workflows.

Engineering 100

Engineering Business Intelligence BI Data Warehouse 100

Data Engineering Podcast

OCTOBER 29, 2021

In this episode Ahmed Elsamadisi shares his journey to find a more flexible and universal data model in the form of the "activity schema" that is powering the Narrator platform, and how it has allowed his customers to perform self-service exploration of their business domains without being blocked by schema evolution in the data warehouse.

Data Warehouse 100

Data Warehouse BI Data Workflow Data Engineering 100

Ascend.io

OCTOBER 28, 2024

You might even think of effective data transformation like a powerful magnet that draws the needle from the stack, leaving the hay behind. In this blog post, we’ll explore fundamental concepts, intermediate strategies, and cutting-edge techniques that are shaping the future of data engineering.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

Data Engineering Podcast

NOVEMBER 27, 2021

If you are having trouble answering questions for your business with the data that you generate and collect internally, then it is definitely worthwhile to explore the information available from external sources. Missing data? Start trusting your data with Monte Carlo today! Struggling with broken pipelines? Stale dashboards?

Data Warehouse 100

Data Warehouse Data Lake BI Business Intelligence 100

Data Engineering Podcast

MAY 3, 2021

Summary The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Data Engineering Podcast

JUNE 1, 2021

This is an interesting conversation about how to make data more accessible and more useful by improving the user experience of the tools that we create. RudderStack’s smart customer data pipeline is warehouse-first. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt.

Data Pipeline 100

Data Pipeline Data Warehouse Data Data Workflow 100

Data Engineering Podcast

JULY 3, 2022

Summary The perennial challenge of data engineers is ensuring that information is integrated reliably. In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. Data teams are increasingly under pressure to deliver.

Data Integration 100

Data Integration MongoDB Scala MySQL 100

Data Engineering Podcast

OCTOBER 15, 2021

In this episode they discuss the recent work that has been done by the community, how their work is building on top of that foundation, and how you can get started with DataHub for your own work to manage data discovery today. Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads?

Metadata 100

Metadata BI Data Warehouse Government 100

Christophe Blefari

SEPTEMBER 28, 2023

We need to store, process and visualise data, everything else is just marketing. I often say that data engineering is boring, insanely boring. When you are a data engineer you're getting paid to build systems that people can rely on. For downstream data quality there are also a lot of tools.

Big Data 147

Big Data Cloud Storage Hadoop SQL 147

Data Engineering Podcast

APRIL 5, 2021

She explains how the design of the platform is informed by the needs of managing data projects for large and small teams across her previous roles, how it integrates with your existing systems, and how it can work to bring everyone onto the same page. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Databand.ai

AUGUST 30, 2023

Here are some of the reasons why DataOps tools are important: Improved Collaboration DataOps tools enable better collaboration between data teams, including data engineers, data scientists, and data analysts.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Workfall

JULY 4, 2023

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. With this 3rd platform generation, you have more real time data analytics and a cost reduction because it is easier to manage this infrastructure in the cloud thanks to managed services.

Technology 130

Technology Architecture Google Cloud Metadata 130

Data Engineering Podcast

JULY 17, 2022

In this episode Crux CTO Mark Etherington discusses the different costs involved in managing external data, how to think about the total return on investment for your data, and how the Crux platform is architected to reduce the toil involved in managing third party data. Tired of deploying bad data?

Data Management 130

Data Management Management Metadata MongoDB 130

Data Engineering Podcast

SEPTEMBER 4, 2022

In this episode Gopal Erinjippurath discusses the data engineering challenges of building and serving those data sets, and how they are distilling complex climate information into consumable facts so you don’t have to be an expert to understand it. Go to dataengineeringpodcast.com/ascend and sign up for a free trial.

MongoDB 100

MongoDB Scala MySQL Machine Learning 100

Data Engineering Podcast

JULY 31, 2022

Summary Data lineage is the roadmap for your data platform, providing visibility into all of the dependencies for any report, machine learning model, or data warehouse table that you are working with. For more information on Prefect, visit dataengineeringpodcast.com/prefect.

IT 100

IT Metadata MongoDB MySQL 100

Workfall

JUNE 12, 2023

In this dynamic realm of data engineering, a monumental challenge takes centre stage: efficiently managing the ever-changing tides of real-time data. Data, the lifeblood of organisations, holds the key to unlocking untapped potential and propelling businesses forward. Where Is CDC Used and Who Uses It?

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Ascend.io

MAY 31, 2023

In the modern world of data engineering, two concepts often find themselves in a semantic tug-of-war: data pipeline and ETL. Data Transformation Because of the many variations of source systems, the data collected during the ingestion phase is often raw, messy, and unstructured.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

Monte Carlo

APRIL 28, 2023

Data Engineers : Data engineers who work with data pipelines and ETL processes use DuckDB to enhance the performance of their data processing tasks. They can leverage DuckDB’s efficient query execution and integration with data formats like Parquet to optimize their data workflows.

Data Warehouse 52

Data Warehouse Programming Language Datasets Database 52

Ascend.io

DECEMBER 19, 2023

You’re extracting and loading data first, then transforming it in Snowflake’s cloud data warehouse. These features make Snowflake a great solution for data engineering, but the question arises: why might you still need a separate ETL tool? that you can combine to create custom data workflows.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Monte Carlo

JULY 27, 2023

Data quality engineers also need to have experience operating in cloud environments and using many of the modern data stack tools that are utilized in building and maintaining data pipelines. 78% of job postings referenced at least part of their environment was in a modern data warehouse, lake, or lakehouse.

Engineering 52

Engineering Healthcare Data Warehouse Scala 52

Meltano

OCTOBER 5, 2022

In the same way, a DataOps engineer designs the data assembly line that enables data scientists to derive insights from data analytics faster and with fewer errors. DataOps engineers improve the speed and quality of the data development process by applying DevOps principles to data workflow, known as DataOps.

Engineering 52

Engineering Raw Data Data Pipeline ETL Tools 52

Monte Carlo

MAY 25, 2023

However, once data platforms scale beyond a certain point, managing jobs will quickly become unwieldy by in-house standards. Data orchestration supports the data engineering lifecycle by bringing automation to data pipelines. Source : Fundamentals of Data Engineering by Joe Reis and Matt Housley.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Monte Carlo

JUNE 14, 2023

For example, data may be extracted from multiple sources, reorganized, and joined at different times before ultimately being served to its final destination. Why is data pipeline architecture important? Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Monte Carlo

AUGUST 13, 2024

Here’s how Gartner officially defines the category of data observability tools: “Data observability tools are software applications that enable organizations to understand the state and health of their data, data pipelines, data landscapes, data infrastructures, and the financial operational cost of the data across distributed environments.

Data 52

Data Data Warehouse Data Pipeline Data Architecture 52