Data Engineering Podcast

APRIL 21, 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 182

Data Lake High Quality Data Data Pipeline Machine Learning 182

Data Engineering Podcast

MAY 26, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Powered by Trino, the query engine Apache Iceberg was designed for, Starburst is an open platform with support for all table formats including Apache Iceberg, Hive, and Delta Lake.

Systems 130

Systems Data Lake High Quality Data Google Cloud 130

Data Engineering Podcast

FEBRUARY 4, 2024

Summary Stream processing systems have long been built with a code-first design, adding SQL as a layer on top of the existing framework. 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

Data Engineering Podcast

FEBRUARY 18, 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 262

Data Lake High Quality Data Data Warehouse Google Cloud 262

Data Engineering Podcast

JANUARY 28, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. SIEM) A query engine is useless without data to analyze. What are the data acquisition paths/sources that you are designed to work with?-

Data Lake 147

Data Lake Building High Quality Data AWS 147

Data Engineering Podcast

MARCH 24, 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 162

Data Lake High Quality Data Hadoop Data Pipeline 162

Data Engineering Podcast

DECEMBER 24, 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.

Kafka 245

Kafka Data Lake High Quality Data SQL 245

Data Engineering Podcast

FEBRUARY 25, 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.

Database 162

Database Technology Data Lake High Quality Data 162

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

Data Engineering Podcast

MARCH 3, 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.

Programming 162

Programming Data Lake High Quality Data Data Pipeline 162

Data Engineering Podcast

DECEMBER 17, 2023

Summary The "modern data stack" promised a scalable, composable data platform that gave everyone the flexibility to use the best tools for every job. Data lakes are notoriously complex. What are the points of friction that teams run into when trying to build their data platform? Data lakes are notoriously complex.

Data Lake 130

Data Lake High Quality Data SQL Architecture 130

Data Engineering Podcast

NOVEMBER 26, 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.

Architecture 147

Architecture Data Lake High Quality Data SQL 147

Data Engineering Podcast

DECEMBER 10, 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.

Data Lake 130

Data Lake High Quality Data SQL Architecture 130

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. How do we build data products ? How can we interoperate between the data domains ? As you can see, this is in the code part where you are building your data pipelines, a misnomer because this is an over simplification.

Technology 130

Technology Architecture Google Cloud Metadata 130

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. By improving data quality, organizations can generate more accurate and reliable insights from their data analytics processes.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 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

Data Engineering Podcast

NOVEMBER 5, 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.

PostgreSQL 147

PostgreSQL Data Lake High Quality Data SQL 147

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Monte Carlo

MAY 14, 2024

Let’s revisit how several of those key table formats have emerged and developed over time: Apache Avro : Developed as part of the Hadoop project and released in 2009, Apache Avro provides efficient data serialization with a schema-based structure.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Data Engineering Podcast

NOVEMBER 12, 2023

Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Data lakes are notoriously complex. How have the design and goals of the product changed since you first started working on it? 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

Databand.ai

AUGUST 30, 2023

Integrating these principles with data operation-specific requirements creates a more agile atmosphere that supports faster development cycles while maintaining high quality standards. Technical Challenges Choosing appropriate tools and technologies is critical for streamlining data workflows across the organization.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

Monte Carlo

MAY 25, 2023

Automated data orchestration removes data bottlenecks by eliminating the need for manual data preparation, enabling analysts to both extract and activate data in real-time. Improved data governance. Automating data workflows. Faster time to insights for data analysts. What is Prefect?

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

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. What portions of the data workflow is Atlan responsible for?

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional who is in charge of designing, implementing, and maintaining data processing systems and solutions on the Microsoft Azure cloud platform. A Data Engineer is responsible for designing the entire architecture of the data flow while taking the needs of the business into account.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Monte Carlo

JULY 27, 2023

The data quality engineer will then need to design or optimize data architectures and pipelines to ensure these requirements are met (mentioned in 70% of job postings). This is a continuous process as data environments are complex, interdependent, and constantly changing. Ability to perform root cause analysis on defects.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Data Engineering Podcast

OCTOBER 15, 2021

Summary The binding element of all data work is the metadata graph that is generated by all of the workflows that produce the assets used by teams across the organization. The DataHub project was created as a way to bring order to the scale of LinkedIn’s data needs. How is the governance of DataHub being managed?

Metadata 100

Metadata BI Data Warehouse Government 100

Ascend.io

DECEMBER 19, 2023

The GUI often allows for easy visualization and monitoring of data pipelines, which can be particularly beneficial for complex workflows. However, there is a critical aspect to consider: using Snowflake’s native features to build data pipelines is akin to constructing a bespoke data platform.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Databand.ai

DECEMBER 29, 2022

Follow Ravit on LinkedIn 5) Priya Krishnan Head of Product Management, Data and AI at IBM Priya is an innovative, customer-focused, data-driven product executive with over 16 years of experience in global product management, strategy, and GTM roles to commercialize and monetize in-demand enterprise solutions.

BI 52

BI Consulting Data Science Data Governance 52

Monte Carlo

SEPTEMBER 20, 2022

Here’s how Prefect , Series B startup and creator of the popular data orchestration tool, harnessed the power of data observability to preserve headcount, improve data quality and reduce time to detection and resolution for data incidents. Scaling data governance. This left Dylan’s team with a gap to fill.

Big Data 52

Big Data Data Warehouse Data Data Governance 52

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? It is like a smart scheduler for your data workflows.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Cloudera

AUGUST 21, 2020

The governance aspect is perhaps even more important and businesses need to be able to understand where the data comes from. Data lineage, personally identifiable information or PPI and metadata all fall under a broad data governance banner which is critically important in terms of what needs to be protected and mapped out.

Banking 62

Banking Kafka Cloud Storage Government 62

phData: Data Engineering

JANUARY 3, 2022

How do I know that my data is accurate and complete? You have to have talented engineers who can design and implement systems to ensure that your product remains highly available. You need additional resources to monitor and validate that your system is working as designed.

IT 52

IT AWS Software Engineer Software Engineering 52

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs.

Consulting 120

Consulting Machine Learning Data Science Data Pipeline 120

AltexSoft

MARCH 14, 2023

Built around a cloud data warehouse, data lake, or data lakehouse. Modern data stack tools are designed to integrate seamlessly with cloud data warehouses such as Redshift, Bigquery, and Snowflake, as well as data lakes or even the child of the first two — a data lakehouse.

IT 59

IT Data Warehouse Data Governance Data Lake 59