Towards Data Science

APRIL 6, 2023

Today’s post follows the same philosophy: fitting local and cloud pieces together to build a data pipeline. And, when it comes to data engineering solutions, it’s no different: They have databases, ETL tools, streaming platforms, and so on — a set of tools that makes our life easier (as long as you pay for them). not sponsored.

Data Pipeline 75

Data Pipeline AWS Amazon Web Services Python 75

Monte Carlo

AUGUST 25, 2023

One advantage of data warehouses is their integrated nature. As fully managed solutions, data warehouses are designed to offer ease of construction and operation. A warehouse can be a one-stop solution, where metadata, storage, and compute components come from the same place and are under the orchestration of a single vendor.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Marc Lamberti

SEPTEMBER 22, 2023

Stemming from this analogy, “you” is the orchestrator in data orchestration, and the recipe is the data pipeline. Digging into the official definition, you will see that Airflow is noted as an open-source platform that programmatically authors, schedules, and monitors workflows. What is a Task?

Data Pipeline 130

Data Pipeline Python Metadata Database 130

Data Engineering Podcast

NOVEMBER 6, 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. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc.,

Building 130

Building IT Metadata MongoDB 130

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Validity: Validity refers to whether the data accurately represents the concepts or phenomena it is intended to measure.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Christophe Blefari

JANUARY 20, 2024

Who are the data engineers? Every company out there has his own definition for the data engineer role. In my opinion we can easily say a data engineer is a software engineer working with data. The idea behind is to solve data problem by building software. This post shows the data job market with numbers.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Monte Carlo

AUGUST 30, 2023

Your test passes when there are no rows returned, which indicates your data meets your defined conditions. Run the test again to validate that the initial problem is solved and that your data meets your quality and accuracy standards. This will help you debug any failures and understand the health of your data pipeline over time.

SQL 52

SQL Datasets Database High Quality Data 52

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

JANUARY 18, 2021

In every step,we do not just read, transform and write data, we are also doing that with the metadata. Last part, it was added the data security and privacy part. Every data governance policy about this topic must be read by a code to act in your data platform (access management, masking, etc.)

Data Governance 130

Data Governance Metadata Government Data Pipeline 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. If you look at the BigQuery service (the cloud data warehouse in the Google Cloud Platform), you can start to write, transform and query your data without any provisioning. What you have to code is this workflow !

Technology 130

Technology Architecture Google Cloud Metadata 130

Cloudera

MARCH 9, 2021

In this blog, we’ll highlight the key CDP aspects that provide data governance and lineage and show how they can be extended to incorporate metadata for non-CDP systems from across the enterprise. The SDX layer of CDP leverages the full spectrum of Atlas to automatically track and control all data assets. Assets: Files.

Data Governance 95

Data Governance Government Metadata Datasets 95

Data Engineering Weekly

JULY 7, 2024

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. The author highlights Paimon’s consistency model by examining the metadata model.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Lake 98

Monte Carlo

OCTOBER 2, 2024

Something’s definitely wrong. Without Python data lineage, you could waste valuable time hunting through databases, running SQL queries to trace the numbers back to their source. Marquez is a metadata service that implements the OpenLineage API. It’s 9am and you’re rushing to generate a report for your 10 a.m.

Python 52

Python Datasets Metadata Data 52

Monte Carlo

MAY 14, 2024

At its core, a table format is a sophisticated metadata layer that defines, organizes, and interprets multiple underlying data files. Table formats incorporate aspects like columns, rows, data types, and relationships, but can also include information about the structure of the data itself.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Data Engineering Podcast

MAY 22, 2022

What is the workflow for someone building a new pipeline in Flyte? What are the patterns that you and the community have established to encourage discovery and reuse of granular task definitions? What are the available interfaces for extending Flyte and building integrations with other components across the data ecosystem?

Machine Learning 130

Machine Learning Data Engineering Data Engineer Cloud 130

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

Snowflake

DECEMBER 12, 2023

To give customers flexibility for how they fit Snowflake into their data architecture, Iceberg Tables can be configured to use either Snowflake or an external service such as AWS Glue as the table’s catalog to track metadata, with an easy, one-line SQL command to convert the table’s catalog to Snowflake in a metadata-only operation.

Metadata 94

Metadata Python AWS Government 94

Monte Carlo

MARCH 4, 2024

To do this, data observability solutions like Monte Carlo will piece together information from query logs, data aggregates and system metadata. All of this is to say that how table formats are structured, and specifically the metadata, impacts how they can be monitored and observed.

Metadata 52

Metadata Data Lake Data Engineering Data Engineer 52

Netflix Tech

JULY 22, 2024

What is Maestro Maestro is a general-purpose, horizontally scalable workflow orchestrator designed to manage large-scale workflows such as data pipelines and machine learning model training pipelines. A workflow definition is defined in a JSON format. a sub-graph) defined within the foreach definition block.

Machine Learning 102

Machine Learning Coding Data Pipeline Java 102

Databand.ai

AUGUST 30, 2023

A shorter time-to-value indicates that your organization is efficient at processing and analyzing data for decision-making purposes. Monitoring this metric helps identify bottlenecks in the data pipeline and ensures timely insights are available for business users.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Data Engineering Podcast

JANUARY 7, 2022

Summary Data observability is a set of technical and organizational capabilities related to understanding how your data is being processed and used so that you can proactively identify and fix errors in your workflows. Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud.

BI 100

BI Data Warehouse Metadata SQL 100

Airbnb Tech

NOVEMBER 28, 2023

To fully enable this incentivization approach, we believed it would be paramount to introduce the concept of a data quality score directly tied to data assets. We identified the following objectives for the score: Evolve our understanding of data quality beyond a simple binary definition (certified vs uncertified).

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

Ascend.io

FEBRUARY 28, 2024

Engineers ensure the availability of clean, structured data, a necessity for AI systems to learn from patterns, make accurate predictions, and automate decision-making processes. Through the design and maintenance of efficient data pipelines , data engineers facilitate the seamless flow and accessibility of data for AI processing.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Data Engineering Podcast

FEBRUARY 6, 2022

Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. You can observe your pipelines with built in metadata search and column level lineage.

Data Pipeline 100

Data Pipeline Data Metadata Software Engineer 100

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. Those who don’t embrace it will be left behind. With the right prompt (this is key!),

Pipeline-centric 52

Pipeline-centric Database-centric Metadata Unstructured Data 52

Cloudera

MARCH 31, 2021

Customers who have chosen Google Cloud as their cloud platform can now use CDP Public Cloud to create secure governed data lakes in their own cloud accounts and deliver security, compliance and metadata management across multiple compute clusters. Data Preparation (Apache Spark and Apache Hive) .

Google Cloud 93

Google Cloud Cloud Amazon Web Services Cloud Storage 93

Netflix Tech

JANUARY 25, 2023

Scalable Annotation Service — Marken by Varun Sekhri , Meenakshi Jindal Introduction At Netflix, we have hundreds of micro services each with its own data models or entities. For example, we have a service that stores a movie entity’s metadata or a service that stores metadata about images. In this case it is BOUNDING_BOX.

Algorithm 115

Algorithm Media Metadata Data Ingestion 115

Monte Carlo

AUGUST 13, 2024

And how should you leverage it as you evaluate different approaches to improving data quality and reliability? The Gartner definition of data observability While Monte Carlo is unquestionably the data observability category’s creator, we’re certainly not the only game in town – and that’s a good thing. Let’s get into it.

Data 52

Data Data Warehouse Data Pipeline Data Architecture 52

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

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

Monte Carlo

AUGUST 10, 2023

What is Data observability? The five pillars My data observability definition has not changed since I first coined it in 2019: Data observability refers to an organization’s comprehensive understanding of the health and performance of the data within their systems.

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

Netflix Tech

JANUARY 10, 2024

The Netflix video processing pipeline went live with the launch of our streaming service in 2007. By integrating with studio content systems, we enabled the pipeline to leverage rich metadata from the creative side and create more engaging member experiences like interactive storytelling.

Process 93

Process Pipeline-centric Media Metadata 93

Monte Carlo

FEBRUARY 9, 2023

Pie Insurance , a leading small business insurtech, leverages a data vault 2.0 architecture (with some minor deviations) to achieve their data integration objectives around scalability and use of metadata. “A How do you implement a data vault architecture? This layer has minimal transformation rules.

Architecture 52

Architecture Raw Data Metadata Data Warehouse 52

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Second, you must establish a definition of “done.” In DataOps, the definition of done includes more than just some working code. Figure 1 shows a manually executed data analytics pipeline.

Engineering 61

Engineering Business Analyst BI Metadata 61

François Nguyen

MARCH 7, 2021

In this article, Juan Sequada gives maybe one of the best definition of Data Mesh ” It is paradigm shift towards a distributed architecture that attempts to find an ideal balance between centralization and decentralization of metadata and data management.”

Government 130

Government Data Governance Data Metadata 130

Monte Carlo

MARCH 12, 2024

With Dataplex, teams get lineage and visibility into their data management no matter where it’s housed, centralizing the security, governance, search and discovery across potentially distributed systems. Dataplex works with your metadata. Courtesy of Google Cloud.

Google Cloud 52

Google Cloud Metadata SQL Data Lake 52

Data Engineering Podcast

OCTOBER 28, 2019

In this episode he explains his motivation for creating a product for data management, how the programming model simplifies the work of building testable and maintainable pipelines, and his vision for the future of data programming. If you are building dataflows then Dagster is definitely worth exploring.

Building 100

Building Data Pipeline Programming Language Metadata 100