Cloudyard

MARCH 4, 2023

DCDW Architecture Above all, Architecture was divided into three Business layers: Firstly,Agile Data ingestion : Heterogeneous Source System fed the data into Cloud. Respective Cloud would consume/Store the data in bucket or containers. Load the data AS-IS into Snowflake called RAW layer.

Architecture 52

Architecture Cloud Metadata Data Ingestion 52

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 86

Government Data Ingestion Data PostgreSQL 86

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.

Data Ingestion 52

Data Ingestion Data Pipeline Metadata Utilities 52

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

phData: Data Engineering

SEPTEMBER 19, 2023

By employing robust data modeling techniques, businesses can unlock the true value of their data lake and transform it into a strategic asset. With many data modeling methodologies and processes available, choosing the right approach can be daunting. Want to learn more about data governance?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

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 101

Architecture Metadata Machine Learning Unstructured Data 101

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

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

LinkedIn Engineering

JUNE 15, 2023

In this blog post, we will discuss the AvroTensorDataset API, techniques we used to improve data processing speeds by up to 162x over existing solutions (thereby decreasing overall training time by up to 66%), and performance results from benchmarks and production. The bytes are decoded based on the provided features metadata (i.e.

Datasets 102

Datasets Bytes Process Data Ingestion 102

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. Accelerated Data Analytics DataOps tools help automate and streamline various data processes, leading to faster and more efficient data analytics.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Data Engineering Weekly

OCTOBER 30, 2022

I found the blog helpful in understanding the generative model’s historical development and the path forward. link] Sponsored- [New eBook] The Ultimate Data Observability Platform Evaluation Guide Considering investing in a data quality solution? The author explains how to dump the history of blockchains into S3.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Cloudyard

APRIL 4, 2024

However, the volume of daily transaction data poses challenges in effectively segmenting customers and optimizing engagement. This blog post explores how Snowpark, a powerful tool for data processing within Snowflake, can be used to perform RFM segmentation and unlock actionable customer insights.

Retail 40

Retail Data Ingestion Metadata Database 40

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. 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.

Machine Learning 136

Machine Learning Datasets Data Science Raw Data 136

Netflix Tech

MARCH 25, 2019

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Therefore, the ingestion approach for data lineage is designed to work with many disparate data sources. push or pull.

Building 89

Building Metadata Transportation Data Ingestion 89

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 87

Data Lake Business Intelligence Metadata Data Warehouse 87

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

Data Engineering Weekly

MAY 16, 2023

In the second part, we will focus on architectural patterns to implement data quality from a data contract perspective. Why is Data Quality Expensive? I won’t bore you with the importance of data quality in the blog. In the 'Write' stage, we capture the computed data in a log or a staging area.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

LinkedIn Engineering

DECEMBER 12, 2023

Pinot is a columnar OLAP store that serves analytics queries on data ingested from realtime streams. PEDAL also consists of a metadata store that holds various algorithmic parameters, including the scale of noise that we introduce and whether we should use one-shot or continual observation algorithms.

Algorithm 77

Algorithm Metadata SQL Datasets 77

Cloudera

APRIL 22, 2021

Collects and aggregates metadata from components and present cluster state. Metadata in cluster is disjoint across components. Cloudera will publish separate blog posts with results of performance benchmarks. Cisco Data Intelligence Platform. The post Apache Ozone and Dense Data Nodes appeared first on Cloudera Blog.

Pipeline-centric 100

Pipeline-centric Data Lake Hadoop Metadata 100

Cloudera

FEBRUARY 8, 2021

This is part 2 in this blog series. You can read part 1, here: Digital Transformation is a Data Journey From Edge to Insight. 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.

Data Pipeline 117

Data Pipeline Building Manufacturing Data Warehouse 117

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

DataKitchen

OCTOBER 20, 2023

Example 2: The Data Engineering Team Has Many Small, Valuable Files Where They Need Individual Source File Tracking In a typical data processing workflow, tracking individual files as they progress through various stages—from file delivery to data ingestion—is crucial.

Insurance 69

Insurance Pharmaceutical Data Data Ingestion 69

ProjectPro

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

AWS 98

AWS Scala Metadata Data Lake 98

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

OCTOBER 19, 2020

In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. We could also get contextual information about the streaming session by joining relevant traces with account metadata and service logs. The high data ingestion rate eventually degraded both read and write operations.

Building 94

Building Transportation Java Metadata 94

Ascend.io

JUNE 8, 2023

However, transforming data into a product so that it can deliver outsized business value requires more than just a mission statement; it requires a solid foundation of technical capabilities and a truly data-centric culture. This multitude of sources often causes a dispersed, complex, and poorly structured data landscape.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

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

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 80

Data Warehouse Database-centric Metadata Cloud 80

LinkedIn Engineering

JULY 27, 2023

Costwiz provides a unified experience that helps leaders drive more accurate forecasting of Azure budgets at LinkedIn with resource ownership detection, accountability, expedited remedies, and holistic data visibility (via custom dashboards). ETL processes must determine where to pick up the next batch of data.

Metadata 75

Metadata Utilities Cloud Data Lake 75

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

Cloudera

FEBRUARY 16, 2022

Weak model lineage can result in reduced model performance, a lack of confidence in model predictions and potentially violation of company, industry or legal regulations on how data is used. . Within the CML data service, model lineage is managed and tracked at a project level by the SDX. Figure 03: lineage.yaml.

Machine Learning 83

Machine Learning Algorithm Government Metadata 83

Maxime Beauchemin

JANUARY 20, 2017

The fact that ETL tools evolved to expose graphical interfaces seems like a detour in the history of data processing, and would certainly make for an interesting blog post of its own. Sure, there’s a need to abstract the complexity of data processing, computation and storage.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Rockset

JUNE 6, 2023

In this blog, we’ll walk through how Rockset provides compute-storage separation while making real-time data available to queries. Virtual instances (VIs) are allocations of compute and memory resources responsible for data ingestion, transformations, and queries.

Metadata 52

Metadata Datasets Architecture Algorithm 52

Cloudera

JUNE 4, 2018

The solution to this massive data challenge embedded the Aspire Content Processing Framework into the Cloudera Enterprise Data Hub as a Cloudera Parcel – a binary distribution format containing the program files, along with additional metadata used by Cloudera Manager.

Pharmaceutical 45

Pharmaceutical Unstructured Data Electronics Metadata 45

Databand.ai

JUNE 28, 2023

Data observability works with your data pipeline by providing insights into how your data flows and is processed from start to end. Here is a more detailed explanation of how data observability works within the data pipeline: Data ingestion : Observability begins from the point where data is ingested into the pipeline.

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Netflix Tech

DECEMBER 14, 2018

In the previous blog posts in this series, we introduced the N etflix M edia D ata B ase ( NMDB ) and its salient “Media Document” data model. 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.

Media 96

Media Database Metadata Data Schemas 96

Snowflake

MAY 22, 2023

Cross-Cloud Snowgrid Account Replication expands replication beyond databases – general availability Account Replication, now generally available, expands replication beyond databases to account metadata and integrations, making business continuity truly turnkey. Read our announcement blog post for more.

Healthcare 52

Healthcare Medical Scala Transportation 52