Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineering 52

Cloudera

FEBRUARY 8, 2021

Below is the entire set of steps in the data lifecycle, and each step in the lifecycle will be supported by a dedicated blog post(see Fig. 1): Data Collection – data ingestion and monitoring at the edge (whether the edge be industrial sensors or people in a vehicle showroom).

Data Pipeline 116

Data Pipeline Building Manufacturing Data Warehouse 116

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. What is the workflow for someone getting Sifflet integrated into their data stack?

Data Lake 130

Data Lake Data Ingestion MongoDB Google Cloud 130

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. Hudi seems to be a de facto choice for CDC data lake features. Notion migrated the insert heavy workload from Snowflake to Hudi.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Lake 98

Precisely

MAY 13, 2024

A true enterprise-grade integration solution calls for source and target connectors that can accommodate: VSAM files COBOL copybooks open standards like JSON modern platforms like Amazon Web Services ( AWS ), Confluent , Databricks , or Snowflake Questions to ask each vendor: Which enterprise data sources and targets do you support?

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

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

Cloudyard

JULY 16, 2024

Read Time: 3 Minute, 11 Second This blog post showcases a real-time data pipeline built in Snowflake that leverages Slowly Changing Dimensions (SCD 2) and Finalizer Tasks to ensure your customer data is always fresh, accurate, and reflects historical changes. Snowflake’s Finalizer Tasks come to the rescue!

Data Ingestion 52

Data Ingestion Data Pipeline Metadata Utilities 52

Ascend.io

FEBRUARY 6, 2023

Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products. Data Plane – is the data cloud where the data pipeline workload runs, like Databricks, BigQuery, and Snowflake.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Ascend.io

FEBRUARY 6, 2023

Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products. Data Plane – is the data cloud where the data pipeline workload runs, like Databricks, BigQuery, and Snowflake.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Christophe Blefari

JANUARY 20, 2024

Then here a list of global resources that can help you navigate through the field: The Data Engineer Roadmap — An image with advices and technology names to watch. Reddit r/dataengineering wiki a place where some data eng definitions are written. workflows (Airflow, Prefect, Dagster, etc.) Is it really modern?

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

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.

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Data Security Metadata MongoDB Scala 100

Data Engineering Weekly

MAY 16, 2023

I won’t bore you with the importance of data quality in the blog. Instead, Let’s examine the current data pipeline architecture and ask why data quality is expensive. Instead of looking at the implementation of the data quality frameworks, Let's examine the architectural patterns of the data pipeline.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

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. These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share, and manage their data assets.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Monte Carlo

APRIL 4, 2023

That’s why, in addition to integrating with your central data warehouse , lake , and lakehouse , Monte Carlo also integrates with transformation , orchestration , and now data ingestion tools. Now teams can instantly get full visibility into how these systems may be impacting their data assets, all in a single pane of glass.

BI 64

BI Data Ingestion Data Pipeline Metadata 64

Cloudera

DECEMBER 15, 2022

Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), Cloudera customers, such as Teranet , have built open lakehouses to future-proof their data platforms for all their analytical workloads. Only metadata will be regenerated. Data quality using table rollback.

Cloud 78

Cloud Metadata Google Cloud Data Warehouse 78

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.

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Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Cloudera

DECEMBER 20, 2021

In the past year, the Bank of the West has begun using the Cloudera platform to establish a data governance and security framework to manage and protect its customers’ sensitive information. The platform is centralizing the data, data management & governance, and building custom controls for data ingestion into the system.

Government 89

Government Data Security Banking Metadata 89

DataKitchen

OCTOBER 20, 2023

The Solution: ‘Payload’ Data Journeys Traditional Data Observability usually focuses on a ‘process journey,’ tracking the performance and status of data pipelines. ’ It assigns unique identifiers to each data item—referred to as ‘payloads’—related to each event.

Insurance 69

Insurance Pharmaceutical Data Data Ingestion 69

Data Engineering Weekly

OCTOBER 30, 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. Sign up free to test out the tool today.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Booking.com Engineering

DECEMBER 2, 2022

From data ingestion, data science, to our ad bidding[2], GCP is an accelerant in our development cycle, sometimes reducing time-to-market from months to weeks. Data Ingestion and Analytics at Scale Ingestion of performance data, whether generated by a search provider or internally, is a key input for our algorithms.

Systems 52

Systems Cloud MySQL Relational Database 52

Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. How Apache Iceberg tables structure metadata. I think it’s safe to say it’s getting pretty cold in here. Image courtesy of Dremio. So, is Iceberg right for you?

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

Data Engineering Podcast

AUGUST 6, 2022

Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. What are some of the data management considerations that are introduced by vector databases?

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

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) .

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Google Cloud Cloud Amazon Web Services Cloud Storage 92

Rockset

AUGUST 28, 2023

Query across your ANN indexes on vector embeddings, and your JSON and geospatial “metadata” fields efficiently. Spin a Virtual Instance for streaming data ingestion. If you know SQL, you already know how to use Rockset. We obsess about efficiency in the cloud. Spin another completely isolated Virtual Instance for your app.

Metadata 78

Metadata Unstructured Data SQL Database 78

Ascend.io

JUNE 21, 2023

In the case of data products, these are networks of data pipelines, which makes them an integral part of your modern operational machinery. To determine the ROI of any particular data product, you need to attribute the costs of building and running data pipelines.

Pipeline-centric 52

Pipeline-centric Data Pipeline Metadata Data 52

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. ML Pipeline operations begins with data ingestion and validation, followed by transformation.

Data Preparation 46

Data Preparation Datasets Metadata Data Ingestion 46

Netflix Tech

MARCH 25, 2019

You are about to make structural changes to the data and want to know who and what downstream to your service will be impacted. Finally, imagine yourself in the role of a data platform reliability engineer tasked with providing advanced lead time to data pipeline (ETL) owners by proactively identifying issues upstream to their ETL jobs.

Building 89

Building Metadata Transportation Data Ingestion 89

RandomTrees

SEPTEMBER 6, 2020

The architecture is three layered: Database Storage: Snowflake has a mechanism to reorganize the data into its internal optimized, compressed and columnar format and stores this optimized data in cloud storage. This stage handles all the aspects of data storage like organization, file size, structure, compression, metadata, statistics.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 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. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

ProjectPro

FEBRUARY 25, 2022

Efficient Scheduling and Runtime Increased Adaptability and Scope Faster Analysis and Real-Time Prediction Introduction to the Machine Learning Pipeline Architecture How to Build an End-to-End a Machine Learning Pipeline? Is python suitable for machine learning pipeline design patterns?

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

Ascend.io

MAY 19, 2023

In our case, data ingestion, transformation, orchestration, reverse ETL, and observability. This is the modern data stack as we know it today. The modern data stack has become disjointed and complex, slowing data engineering’s productivity and limiting their ability to provide value to the business.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

Netflix Tech

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster.

Data Warehouse 83

Data Warehouse Metadata Algorithm Data 83

Silectis

JANUARY 3, 2021

Data Catalog An organized inventory of data assets relying on metadata to help with data management. Data Engineering Data engineering is a process by which data engineers make data useful. Data Integration Combining data from various, disparate sources into one unified view.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Cloudera

SEPTEMBER 28, 2020

Cloudera delivers an enterprise data cloud that enables companies to build end-to-end data pipelines for hybrid cloud, spanning edge devices to public or private cloud, with integrated security and governance underpinning it to protect customers data. Data Science and machine learning workloads using CDSW.

Cloud 132

Cloud Kafka Professional Services Metadata 132

Rockset

NOVEMBER 28, 2022

First, CDC theoretically allows companies to analyze and react to data in real time, as it’s generated. It works with existing streaming systems like Apache Kafka, Amazon Kinesis, and Azure Events Hubs, making it easier than ever to build a real-time data pipeline. Striim : A unified data integration and streaming platform.

Data Warehouse 52

Data Warehouse PostgreSQL MongoDB SQL 52

DataKitchen

SEPTEMBER 21, 2023

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. This existing paradigm fails to address the challenges and intricacies of “Data in Use.” ” For example, these tools may offer metadata-based notifications.

Raw Data 69

Raw Data Data Business Intelligence Data Engineering 69