Ascend.io

APRIL 22, 2024

This initiative is more than just an upgrade; it’s a reimagining of what a Data Automation Platform can be: dynamic, extensible, and highly intelligent. A unified platform that combines a powerful metadata core, an extensible plugin architecture, DataAware automation, and multiple AI Assistants.

Project 52

Project Metadata Data Pipeline Data Engineering 52

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.

Architecture 130

Architecture Technology Google Cloud Metadata 130

Data Engineering Podcast

JULY 10, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

Data Engineering Podcast

FEBRUARY 20, 2022

RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. RudderStack’s smart customer data pipeline is warehouse-first.

Systems 100

Systems Software Engineer Software Engineering Data Warehouse 100

Monte Carlo

APRIL 20, 2024

To do this, you need data monitors that drill “ deep ” into the data using both machine learning and user-defined rules, as well as metadata monitors to scale “ broadly ” across every production table in your environment and to be fully integrated across your stack. What is data quality monitoring?

IT 52

IT Metadata Data Warehouse BI 52

Workfall

JUNE 12, 2023

Moreover, it facilitates the implementation of microservices architectures and event-driven systems, automating reactions to data changes without manual intervention. It captures incremental changes from transactional databases or other sources, efficiently loading them into data warehouses or data lakes.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Data Engineering Podcast

JULY 5, 2021

Summary At the core of every data pipeline is an workflow manager (or several). Deploying, managing, and scaling that orchestration can consume a large fraction of a data team’s energy so it is important to pick something that provides the power and flexibility that you need.

Systems 100

Systems Management Data Warehouse Programming Language 100

Data Engineering Podcast

MARCH 22, 2021

Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Metadata Data Lake Hadoop 100

Data Engineering Podcast

MAY 3, 2021

They explain how to think about your data systems in a holistic and maintainable fashion, the security challenges that threaten to derail your efforts, and the power of using metadata as the foundation of everything that you do. Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Data Engineering Podcast

APRIL 5, 2021

Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Data Engineering Podcast

DECEMBER 16, 2019

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Data Engineering Podcast

JUNE 8, 2021

RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Hadoop Metadata Architecture 100

Netflix Tech

MARCH 7, 2024

Once we have discovered the Parquet files to be processed, MetaflowDataFrame takes over: it downloads data using Metaflow’s high-throughput S3 client directly to the process’ memory, which often outperforms reading of local files. Explainer flow is event-triggered by an upstream flow, such Model A, B, C flows in the illustration.

Systems 90

Systems Media Machine Learning Data Warehouse 90

ProjectPro

FEBRUARY 16, 2023

With so much riding on the efficiency of ETL processes for data engineering teams, it is essential to take a deep dive into the complex world of ETL on AWS to take your data management to the next level. ETL has typically been carried out utilizing data warehouses and on-premise ETL tools.

AWS 52

AWS Data Management ETL Tools Management 52

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. 7 Be Intentional About the Batching Model in Your Data Pipelines Different batching models.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Monte Carlo

MAY 18, 2023

Another consideration for these tools are the integration points across the data pipeline. A question to ask is how will these data lineage source solutions account for dbt models, Fivetran connectors, and their relationship to tables? Rather it’s an open standard for metadata and data lineage collection.

Metadata 64

Metadata Data Lake Government Data Governance 64

Data Engineering Podcast

AUGUST 6, 2022

Summary The optimal format for storage and retrieval of data is dependent on how it is going to be used. For analytical systems there are decades of investment in data warehouses and various modeling techniques. Data stacks are becoming more and more complex. manage versions of vectors, metadata management, etc.)

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

Striim

JANUARY 4, 2023

Companies need to analyze large volumes of datasets, leading to an increase in data producers and consumers within their IT infrastructures. These companies collect data from production applications and B2B SaaS tools (e.g., This data makes its way into a data repository, like a data warehouse (e.g.,

PostgreSQL 52

PostgreSQL Data Warehouse Data Lake Data 52

AltexSoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Data warehouse vs. data lake in a nutshell.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

AUGUST 29, 2022

To do this, you need data monitors that drill “ deep ” into the data using both machine learning and user-defined rules, as well as metadata monitors to scale “ broadly ” across every production table in your environment and to be fully integrated across your stack.

IT 52

IT Metadata Data Warehouse BI 52

Monte Carlo

SEPTEMBER 20, 2022

The complexity of Prefect’s data pipelines underscores the value—and challenges—of achieving data reliability at scale. But a growing company means growing data needs. After some initial cheerleading, Dylan convinced the Prefect team to stand up a Monte Carlo instance for their BigQuery data warehouse.

Big Data 52

Big Data Data Warehouse Data Data Governance 52

Data Engineering Weekly

NOVEMBER 6, 2022

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. Every data processing engine has one metadata store to integrate.

Data Engineering 40

Data Engineering Data Engineer Engineering Machine Learning 40

Netflix Tech

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. Some of the optimizations are prerequisites for a high-performance data warehouse.

Data Warehouse 81

Data Warehouse Metadata Algorithm Data 81

Monte Carlo

AUGUST 17, 2021

Data informs every business decision, from customer support to feature development, and most recently, how to support pricing plans for organizations most affected during COVID-19. When migrating to Snowflake, PagerDuty wanted to understand the health of their data pipelines through fully automated data observability.

Insurance 40

Insurance Retail Data Pipeline Portfolio 40

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? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Cloudera

FEBRUARY 8, 2021

The first blog introduced a mock connected vehicle manufacturing company, The Electric Car Company (ECC), to illustrate the manufacturing data path through the data lifecycle. Having completed the Data Collection step in the previous blog, ECC’s next step in the data lifecycle is Data Enrichment.

Data Pipeline 114

Data Pipeline Building Manufacturing Data Warehouse 114

Data Engineering Podcast

JUNE 17, 2023

Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. What are the most interesting, innovative, or unexpected ways that you have seen column-aware data modeling used?

Data Lake 130

Data Lake Metadata Data Architecture Machine Learning 130

dbt Developer Hub

NOVEMBER 3, 2022

Let’s discuss how to convert events from an event-driven microservice architecture into relational tables in a warehouse like Snowflake. We use Snowflake as our data warehouse where we build dashboards both for internal use and for customers. So our solution was to start using an intentional contract: Events.

Kafka 52

Kafka ETL Tools BI Database 52

Towards Data Science

FEBRUARY 9, 2024

As a result, here’s a short list of what inspired me to write an amendment to my original 2021 article : Scale Companies, big and small, are starting to reach levels of data scale previously reserved for Netflix, Uber, Spotify and other giants creating unique services with data.

Building 82

Building Transportation Data Lake Metadata 82

Data Engineering Podcast

MAY 18, 2021

Summary Data lineage is the common thread that ties together all of your data pipelines, workflows, and systems. In order to get a holistic understanding of your data quality, where errors are occurring, or how a report was constructed you need to track the lineage of the data from beginning to end.

Metadata 100

Metadata Kafka Data Warehouse Hadoop 100

Data Engineering Podcast

NOVEMBER 20, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Lake 130

Data Lake Data Ingestion MongoDB Scala 130

Airbnb Tech

NOVEMBER 28, 2023

However, for all of our uncertified data, which remained the majority of our offline data, we lacked visibility into its quality and didn’t have clear mechanisms for up-leveling it. How could we scale the hard-fought wins and best practices of Midas across our entire data warehouse?

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

Monte Carlo

AUGUST 10, 2023

Data observability tools employ automated monitoring, root cause analysis, data lineage, and data health insights to proactively detect, resolve, and prevent data anomalies. Freshness : Freshness seeks to understand how up-to-date your data tables are, as well as the cadence at which your tables are updated.

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

Monte Carlo

FEBRUARY 8, 2024

GigaOm GigaOm’s Data Observability Radar Report covers the problem data observability tools look to solve saying, “Data observability is critical for countering, if not eliminating, data downtime, in which the results of analytics or the performance of applications are compromised because of unhealthy, inaccurate data.”

BI 59

BI Data Pipeline Data Warehouse Data 59

Monte Carlo

APRIL 4, 2023

Data anomalies and incidents can be introduced across any point of this pipeline, which can make root cause analysis a fragmented and frustrating multi-tab process. Now teams can instantly get full visibility into how these systems may be impacting their data assets, all in a single pane of glass. A modified dbt model?

BI 64

BI Data Ingestion Metadata Data Pipeline 64

Monte Carlo

MAY 31, 2022

When a data pipeline breaks, data engineers need to immediately understand where the rupture occurred and what has been impacted. Data downtime is costly. Field-level data lineage (not necessarily Spark lineage) with hundreds of connections between objects in upstream and downstream tables.

Data Lake 52

Data Lake Building Scala Metadata 52