Datakin

AUGUST 10, 2021

Blog An instant demo of data lineage is worth a thousand words Written by Ross Turk on August 10, 2021 They say that a picture is worth a thousand words. If you’ve ever tried to describe how all the jobs in your data pipeline are interrelated using just words, I am sure it wasn’t easy.

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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.

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Christophe Blefari

JULY 13, 2024

kyutai released Moshi — Moshi is a "voice-enabled AI" The team as kyutai developed the model with an audio interface-first with an audio language model, which make the conversation with the AI more real (demo at 5:00 min) as it can interrupt you or kinda "think" (meaning for predict the next audio segment) while it speaks.

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Kafka AWS Data Database 130

DataKitchen

DECEMBER 17, 2024

The result is a broken, reactive process that fails to prevent data quality issues at their source. Gartners solution emphasizes adopting augmented data quality technologies that use automation, AI/ML-driven insights, and metadata-driven workflows to improve efficiency. Download Now Request Demo

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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. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.

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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.

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Data Engineering Podcast

JULY 3, 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.

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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.

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The Modern Data Company

JANUARY 22, 2024

This mission culminates in the esteemed recognition of honorable mention in Gartner’s 2023 Magic Quadrant for Data Integration, showcasing commitment to excellence and industry leadership in the data-driven era. Data engineering excellence Modern offers robust solutions for building, managing, and operationalizing data pipelines.

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Data Engineering Podcast

JUNE 12, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking all of that information into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Go to dataengineeringpodcast.com/atlan today to learn more about how you can take advantage of active metadata and escape the chaos.

Metadata 100

Metadata Unstructured Data MongoDB Business Intelligence 100

Data Engineering Podcast

JUNE 5, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking all of that information into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Go to dataengineeringpodcast.com/atlan today to learn more about how you can take advantage of active metadata and escape the chaos.

Data Security 100

Data Security Metadata MongoDB MySQL 100

The Modern Data Company

JANUARY 22, 2024

This mission culminates in the esteemed recognition of honorable mention in Gartner’s 2023 Magic Quadrant for Data Integration, showcasing commitment to excellence and industry leadership in the data-driven era. Data engineering excellence Modern offers robust solutions for building, managing, and operationalizing data pipelines.

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Data Integration Metadata Government Unstructured Data 87

Striim

MARCH 4, 2025

Reimagine Data Governance with Sentinel and Sherlock: Striims AI Agents Striim 5.0 introduces Sentinel and Sherlock, which redefine real-time data governance by seamlessly integrating advanced AI capabilities into your data pipelines. Ready to take your data governance efforts to the next level?

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Precisely

JULY 15, 2024

The move to productize data also requires a way to package data products so they are easily and uniformly discoverable and consumed. Data products must be properly designed and organized to be reused across the organization. Data catalogs or data marketplaces can be used to enhance the value of your data mesh strategy.

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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. Let’s dive in!

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Towards Data Science

JULY 8, 2024

Leveraging TensorFlow Transform for scaling data pipelines for production environments Photo by Suzanne D. Williams on Unsplash Data pre-processing is one of the major steps in any Machine Learning pipeline. I have used Colab for this demo, as it is much easier (and faster) to configure the environment.

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Datakin

SEPTEMBER 13, 2021

I’m Ross from Datakin I’d like to show you a new approach to keeping your pipelines running smoothly. Datakin observes your jobs as they run, collecting metadata that helps you understand how data flows through your ecosystem. Blog Datakin in 104 seconds Written by Ross Turk on Sep 13, 2021 Hi!

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Metadata Data Pipeline Datasets Systems 52

Ascend.io

DECEMBER 19, 2022

We hope the real-time demonstrations of Ascend automating data pipelines were a real treat—a long with the special edition T-Shirt designed specifically for the show (picture of our founder and CEO rocking the t-shirt below). Instead, it is a Sankey diagram driven by the same dynamic metadata that runs the Ascend control plane.

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Data Ingestion Metadata Data Pipeline AWS 52

Monte Carlo

JULY 22, 2024

While it shares similarities with software versioning, data versioning has unique characteristics specific to your data management needs. Maintaining metadata about each version. By implementing data versioning, you can create a systematic approach to managing the evolution of your data.

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Data Engineering Weekly

OCTOBER 6, 2024

The learning goes back to the fundamentals of pipeline design principles. Regularly review if pipelines are still required. Minimize the data used in pipelines, aka do incremental data pipeline design. Optimize pipeline schedules. Filter data effectively to make sure the query uses partition pruning.

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The Modern Data Company

JANUARY 17, 2023

These include internal data lakes, apps (1st or 3rd party), web SDK, internal or 3rd-party databases, and even spreadsheets generated within the marketing department. Without lineage or governance metadata, the quality of this data is anyone’s guess. New data is added to the catalog automatically upon ingestion.

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Metadata Government Data Lake Data 59

Ascend.io

MAY 23, 2023

The data engineering world is full of tips and tricks on how to handle specific patterns that recur with every data pipeline. In general, the root of the orphaned data problem lies in the interim storage of data between the individual processing steps that make up a data pipeline.

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Silectis

DECEMBER 15, 2020

We built Magpie with integrated security and data governance features including data lineage, detailed activity tracking, object-level access control, metadata management, scheduling and dependency management, comprehensive reporting, and DevOps support. The time to identify more insights out of huge sets of data.

Data Engineering 52

Data Engineering Data Engineer Engineering ETL Tools 52

Monte Carlo

AUGUST 22, 2023

Slow running data pipelines cost data teams time, money, and goodwill. They utilize excess compute, cause data quality issues, and create a poor user experience for data consumers that must wait in exasperation for data to return, dashboards to load, and AI models to update.

Metadata 52

Metadata Data Pipeline Data Warehouse Utilities 52

Ascend.io

APRIL 7, 2023

With the monolithic architectures most organizations have today, business users are stuck, constantly waiting for new data pipelines to be built or amended based on their requests. Data engineers aren’t huge fans of this paradigm either. Anyone can query the metadata any time anywhere to get the information they need.

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Metadata Data Governance Government Datasets 52

Precisely

JULY 21, 2023

Whether your organization’s focus is improving the customer experience, automating operations, mitigating risk, or accelerating growth and profitability, every initiative relies on data that is trusted to be accurate, consistent, and contextualized.

Data Integration 52

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Rockset

SEPTEMBER 26, 2023

Traditional approaches rely upon cascading batch-oriented data pipelines, meaning data takes hours or even days to flow through the enterprise. As a result, data made available is stale and of low fidelity. We aim to coherently feed these diverse inputs into a model with low latency and without a complex architecture.

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Monte Carlo

AUGUST 6, 2024

Implementing a Proactive Approach: Best Practices To combat these hidden threats in your data warehouse, being proactive is essential. Implement these best practices to enhance your data integrity: Clear Documentation and Metadata Management : Ensure all your data assets and transformations are well-documented.

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Data Engineering Weekly

JULY 2, 2023

Please send your talk proposal today, and let's collaborate to make this the most exciting, insightful, and interactive Data Eng & AI conference yet! LinkedIn write about Hoptimator for auto generated Flink pipeline with multiple stages of systems. Please watch out for this space for the conference registration page soon!!!

Data Engineering 52

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Rockset

APRIL 18, 2023

In real-time ML, the freshness of the features, the serving latency, and the uptime and availability of the data pipeline and model matter. Generating Features for Real-Time Anomaly Detection Now download the anomaly detection dataset to the data / directory. 0 0 Ok, we have the imported data.

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Rockset

AUGUST 26, 2022

When a change is detected, the data is ingested into a data lake hosted on Amazon S3. There, the data is converted, tagged with metadata, cleaned, and de-duplicated in preparation for queries. They demoed but quickly eliminated Snowflake.

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WeCloudData

OCTOBER 19, 2021

It was recommended to pause the crawlers and enable glue metadata registry on the Kinesis level. We proposed a few designed changes, one of the most suitable method is to use Athena and QuickSight for data analytics and data visualization (See appendix for dashboard that WeCloudData team created).

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WeCloudData

OCTOBER 19, 2021

It was recommended to pause the crawlers and enable glue metadata registry on the Kinesis level. We proposed a few designed changes, one of the most suitable method is to use Athena and QuickSight for data analytics and data visualization (See appendix for dashboard that WeCloudData team created).

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Monte Carlo

JANUARY 23, 2024

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. Today’s LLMs can already process enormous amounts of unstructured data, automating much of the monotonous work of data science. It can show me how it built that chart, which dataset it used, and show me the metadata.”

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AltexSoft

MARCH 30, 2023

Source: The Data Team’s Guide to the Databricks Lakehouse Platform Integrating with Apache Spark and other analytics engines, Delta Lake supports both batch and stream data processing. Besides that, it’s fully compatible with various data ingestion and ETL tools.

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Monte Carlo

AUGUST 7, 2023

Prevention: Finally, data observability also provides mechanisms to prevent data issues from happening in the first place, like placing circuit breaking in pipelines and creating visibility around the impact code changes would make on data, among other proactive measures of preventing bad data from entering your pipelines in the first place.

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Monte Carlo

JANUARY 23, 2023

In the case of data observability, however, a single pane of glass is integral to how efficient and effective your data team can be in its data reliability workflows. It’s great to know data moved from point A to point B, but your integration points will paint the full story of what happened to it along the way.

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