phData: Data Engineering

NOVEMBER 8, 2024

First, we create an Iceberg table in Snowflake and then insert some data. Then, we add another column called HASHKEY , add more data, and locate the S3 file containing metadata for the iceberg table. In the screenshot below, we can see that the metadata file for the Iceberg table retains the snapshot history.

Architecture 52

Architecture Systems Data Lake Google Cloud 52

Cloudera

AUGUST 26, 2021

While we walk through the steps one by one from data ingestion to analysis, we will also demonstrate how Ozone can serve as an ‘S3’ compatible object store. Learn more about the impacts of global data sharing in this blog, The Ethics of Data Exchange. Data ingestion through ‘s3’. Ozone Namespace Overview.

Data Science 113

Data Science Cloud Hadoop Metadata 113

Data Engineering Podcast

NOVEMBER 20, 2022

report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. In fact, while only 3.5% That’s where our friends at Ascend.io

Data Lake 130

Data Lake Data Ingestion MongoDB Google Cloud 130

Snowflake

NOVEMBER 11, 2024

For organizations with lakehouse architectures, Snowflake has developed features that simplify the experience of building pipelines and securing data lakehouses with Apache Iceberg™, the leading open source table format. Support for auto-refresh and Iceberg metadata generation is coming soon to Delta Lake Direct.

Data Architecture 104

Data Architecture Architecture Data Lake Kafka 104

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 116

Algorithm Media Metadata Data Ingestion 116

Netflix Tech

MARCH 25, 2019

Therefore, the ingestion approach for data lineage is designed to work with many disparate data sources. Our data ingestion approach, in a nutshell, is classified broadly into two buckets?—?push We leverage Metacat data, our internal metadata store and service, to enrich lineage data with additional table metadata.

Building 90

Building Metadata Transportation Data Ingestion 90

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

Snowflake

NOVEMBER 29, 2023

Also, the associated business metadata for omics, which make it findable for later use, are dynamic and complex and need to be captured separately. Additionally, the fact that they need to be standardized makes the data discovery effort challenging for downstream analysis.

Metadata 97

Metadata Healthcare Medical Data Storage 97

Cloudera

MAY 19, 2021

Data Ingestion. The raw data is in a series of CSV files. We will firstly convert this to parquet format as most data lakes exist as object stores full of parquet files. Parquet also stores type metadata which makes reading back and processing the files later slightly easier. P2 GPU instances are not supported.

Machine Learning 137

Machine Learning Datasets Data Science Raw Data 137

Snowflake

FEBRUARY 13, 2024

Snowpark Updates Model management with the Snowpark Model Registry – public preview Snowpark Model Registry is an integrated solution to register, manage and use models and their metadata natively in Snowflake. Learn more here. Learn more here.

Data Ingestion 102

Data Ingestion AWS Python Metadata 102

Maxime Beauchemin

JANUARY 20, 2017

It also becomes the role of the data engineering team to be a “center of excellence” through the definitions of standards, best practices and certification processes for data objects. In a fast growing, rapidly evolving, slightly chaotic data ecosystem, metadata management and tooling become a vital component of a modern data platform.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

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 91

Government Data Security Banking Metadata 91

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

Confluent

MAY 7, 2019

There is no way that one computer node will ever be able to ingest and process all the events that get generated in real time. We therefore need a way of splitting up the data ingestion work. The broker then waits until that specific __consumer_offsets topic’s partition data gets replicated to all its followers.

Kafka 111

Kafka Accessible Accessibility Metadata 111

Cloudera

SEPTEMBER 9, 2022

With this in mind, it’s clear that no “one size fits all” architecture will work here; we need a diverse set of data services, fit for each workload and purpose, backed by optimized compute engines and tools. . Data changes in numerous ways: the shape and form of the data changes; the volume, variety, and velocity changes.

Architecture 102

Architecture Metadata Machine Learning Unstructured Data 102

Snowflake

JUNE 4, 2024

Governed internal collaboration with better discoverability and AI-powered object metadata Snowflake is introducing an entirely new way for data teams to easily discover, curate and share data, apps and now also models (private preview soon). Getting data ingested now only takes a few clicks, and the data is encrypted.

Government 96

Government Data Ingestion Data PostgreSQL 96

Cloudera

SEPTEMBER 28, 2020

This customer’s workloads leverage batch processing of data from 100+ backend database sources like Oracle, SQL Server, and traditional Mainframes using Syncsort. Data Science and machine learning workloads using CDSW. The customer is a heavy user of Kafka for data ingestion. on roadmap). Instead use Ranger REST API.

Cloud 132

Cloud Kafka Professional Services Metadata 132

Pinterest Engineering

NOVEMBER 7, 2023

Jeff Xiang | Software Engineer, Logging Platform Vahid Hashemian | Software Engineer, Logging Platform Jesus Zuniga | Software Engineer, Logging Platform At Pinterest, data is ingested and transported at petabyte scale every day, bringing inspiration for our users to create a life they love.

Kafka 111

Kafka Java Software Engineer Software Engineering 111

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 94

Google Cloud Cloud Amazon Web Services Cloud Storage 94

LinkedIn Engineering

JUNE 15, 2023

An Avro file is formatted with the following bytes: Figure 1: Avro file and data block byte layout The Avro file consists of four “magic” bytes, file metadata (including a schema, which all objects in this file must conform to), a 16-byte file-specific sync marker, and a sequence of data blocks separated by the file’s sync marker.

Datasets 102

Datasets Bytes Process Data Ingestion 102

Cloudera

APRIL 22, 2021

Collects and aggregates metadata from components and present cluster state. Metadata in cluster is disjoint across components. This architecture allows for: Extremely fast data ingest, and data engineering done at the data lake. Apache Ozone handles both large and small size files. .

Pipeline-centric 101

Pipeline-centric Data Lake Hadoop Metadata 101

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 80

Cloud Metadata Data Warehouse Google Cloud 80

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 84

Data Warehouse Metadata Algorithm Data 84

Rockset

APRIL 18, 2023

Under the hood, Rockset utilizes its Converged Index technology, which is optimized for metadata filtering, vector search and keyword search, supporting sub-second search, aggregations and joins at scale. Feature Generation: Transform and aggregate data during the ingest process to generate complex features and reduce data storage volumes.

Unstructured Data 105

Unstructured Data Metadata Machine Learning SQL 105

Netflix Tech

OCTOBER 19, 2020

Distributed Tracing: the missing context in troubleshooting services at scale Prior to Edgar, our engineers had to sift through a mountain of metadata and logs pulled from various Netflix microservices in order to understand a specific streaming failure experienced by any of our members.

Building 95

Building Transportation Java Metadata 95

Christophe Blefari

JANUARY 20, 2024

The main difference between both is the fact that your computation resides in your warehouse with SQL rather than outside with a programming language loading data in memory. In this category I recommend also to have a look at data ingestion (Airbyte, Fivetran, etc.), workflows (Airflow, Prefect, Dagster, etc.)

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Cloudera

JUNE 30, 2022

With Cloudera’s vision of hybrid data , enterprises adopting an open data lakehouse can easily get application interoperability and portability to and from on premises environments and any public cloud without worrying about data scaling. Why integrate Apache Iceberg with Cloudera Data Platform?

Data Lake 88

Data Lake Business Intelligence Metadata Data Warehouse 88

Towards Data Science

JULY 24, 2023

Closely related to this is how those same platforms are bundling or unbundling related data services from data ingestion and transformation to data governance and monitoring. Why are these things related, and more importantly, why should data leaders care?

Metadata 98

Metadata Data Warehouse Data Lake Data Science 98

Data Engineering Weekly

MAY 16, 2023

WAP [Write-Audit-Publish] Pattern The WAP pattern follows a three-step process Write Phase The write phase results from a data ingestion or data transformation step. In the 'Write' stage, we capture the computed data in a log or a staging area. Event Routers can add additional metadata to the envelope of the event.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

Cloudera

FEBRUARY 8, 2021

ECC will enrich the data collected and will make it available to be used in analysis and model creation later in the data lifecycle. 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.

Data Pipeline 118

Data Pipeline Building Manufacturing Data Warehouse 118

Data Engineering Podcast

AUGUST 6, 2022

report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. manage versions of vectors, metadata management, etc.)

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

ProjectPro

FEBRUARY 8, 2023

When Glue receives a trigger, it collects the data, transforms it using code that Glue generates automatically, and then loads it into Amazon S3 or Amazon Redshift. Then, Glue writes the job's metadata into the embedded AWS Glue Data Catalog. being data exactly matches the classifier, and 0.0 Why Use AWS Glue?

AWS 98

AWS Scala Metadata Data Lake 98

Towards Data Science

DECEMBER 1, 2023

Ingestion —  Fivetran Data ingestion can be configured from both Fivetran and Snowflake using the Partner Connect feature. After the initial sync, you can access your data from the Snowflake UI. You will see the data lineage graph and metadata which is automatically created from your project.

Data Engineering 97

Data Engineering Data Engineer Project Engineering 97

Data Engineering Weekly

MARCH 24, 2024

The APIs support emitting unstructured log lines and typed metadata key-value pairs (per line). Ingestion clusters read objects from queues and support additional parsing based on user-defined regex extraction rules. The extracted key-value pairs are written to the line’s metadata.

Data Engineering 62

Data Engineering Data Engineer Engineering Metadata 62

Cloudera

FEBRUARY 9, 2021

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Data Warehouse 72

Data Warehouse Cloud Kafka Cloud Storage 72

Netflix Tech

DECEMBER 14, 2018

A fundamental requirement for any lasting data system is that it should scale along with the growth of the business applications it wishes to serve. NMDB is built to be a highly scalable, multi-tenant, media metadata system that can serve a high volume of write/read throughput as well as support near real-time queries.

Media 97

Media Database Metadata Data Schemas 97

Towards Data Science

JULY 8, 2024

ML Pipeline operations begins with data ingestion and validation, followed by transformation. The transformed data is trained and deployed. This process also creates a sqlite database for storing the metadata of the pipeline process. Every record and its metadata are stored in dictionary format.

Data Preparation 70

Data Preparation Datasets Metadata Data Ingestion 70