Netflix Tech

NOVEMBER 14, 2023

In the previous installments of this series, we introduced Psyberg and delved into its core operational modes: Stateless and Stateful Data Processing. Pipelines After Psyberg Let’s explore how different modes of Psyberg could help with a multistep data pipeline. Audit Run various quality checks on the staged data.

Metadata 97

Metadata Data Pipeline Scala Data Workflow 97

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 84

Metadata Healthcare Medical Data Storage 84

Precisely

SEPTEMBER 11, 2023

At Precisely, we recognize the value and potential of AI to help our customers work faster and smarter, and make more powerful, confident decisions grounded in trusted data – supporting our overall mission of unlocking data integrity for organizations of all kinds.

Metadata 52

Metadata Data Integration Datasets Data Analysis Tools 52

RandomTrees

FEBRUARY 6, 2024

Serving: Delivering Data with Precision: The seamless process significantly enhances the user experience, allowing for intuitive data exploration and decision-making without requiring technical query language knowledge. The significance of GenAI 1.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

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.

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AWS Scala Metadata Data Lake 98

Data Engineering Weekly

DECEMBER 10, 2023

[link] Netflix: Diving Deeper into Psyberg: Stateless vs Stateful Data Processing Netflix wrote a deep-dive article about Psyberg’s incremental data processing pipeline framework. The blog discusses Psyberg’s two operational models, stateless & stateful data processing.

Data Engineering 76

Data Engineering Data Engineer Engineering Metadata 76

Databand.ai

AUGUST 30, 2023

Data Performance Testing Data performance testing is the process of evaluating the efficiency, effectiveness, and scalability of your data processing systems and infrastructure. To perform data performance testing, you should first establish performance benchmarks and targets for your data processing systems.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Workfall

JUNE 12, 2023

It ensures that organisations stay at the forefront by capturing every twist and turn in the data landscape. With CDC by their side, organisations unlock the power of informed decision-making, safeguard data integrity, and enable lightning-fast analytics. CDC also plays a crucial role in data integration and ETL processes.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Towards Data Science

NOVEMBER 4, 2023

The data engineering landscape is constantly changing but major trends seem to remain the same. How to Become a Data Engineer As a data engineer, I am tasked to design efficient data processes almost every day. It was created by Spotify to manage massive data processing workloads.

Data Engineering 83

Data Engineering Data Engineer Engineering BI 83

Ascend.io

FEBRUARY 28, 2024

Metadata Management for Enhanced Machine Interpretation Data engineering extends its importance to the realm of metadata management, providing AI models with essential context about data definitions, classifications, and governance policies.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

DataKitchen

OCTOBER 20, 2023

The Challenge: High Stakes in the Age of Personalized Data Observability The primary challenge stems from the requirement of Data Consumers for personalized monitoring and alerts based on their unique data processing needs. Data Observability platforms often need to deliver this level of customization.

Insurance 67

Insurance Pharmaceutical Data Data Ingestion 67

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. Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Databand.ai

AUGUST 30, 2023

Challenges of Legacy Data Architectures Some of the main challenges associated with legacy data architectures include: Lack of flexibility: Traditional data architectures are often rigid and inflexible, making it difficult to adapt to changing business needs and incorporate new data sources or technologies.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Cloudera

OCTOBER 7, 2021

In this blog, I will demonstrate the value of Cloudera DataFlow (CDF) , the edge-to-cloud streaming data platform available on the Cloudera Data Platform (CDP) , as a Data integration and Democratization fabric. Data and Metadata: Data inputs and data outputs produced based on the application logic.

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Architecture Metadata Government Kafka 124

The Modern Data Company

APRIL 15, 2024

The Role of a Data Model Explained Think of a data model as the ultimate organizer in the vast library of your company’s data. Its job, from its position near the end of the data processing line, is similar to that of a librarian who: Answers queries from various departments looking for specific insights.

Government 52

Government Metadata Data Data Lake 52

Snowflake

APRIL 5, 2023

To keep the data model in good form, Snowflake allows primary key, foreign key, unique key, and “not null” constraints. However, we may verify the entity relationship and data integrity in the data model using Snowflake Tasks. What is dimensional data modeling? This gives a Data Vault the upperhand in this case.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 52

Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? Reduced reliance on IT Integral to a data fabric is a set of pre-built models and algorithms that expedite data processing.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? Reduced reliance on IT Integral to a data fabric is a set of pre-built models and algorithms that expedite data processing.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

AltexSoft

AUGUST 22, 2022

Support for different data types and use cases. A data fabric supports structured, unstructured, and semi-structured data whether it comes in real-time or generated in batches. It is also driven by metadata, which it uses to recognize patterns, map data, and perform continuous analysis. How data fabric works.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Cloudera

MARCH 23, 2022

Figure 1: Apache Iceberg fits the next generation data architecture by abstracting storage layer from analytics layer while introducing net new capabilities like time-travel and partition evolution. #1: Apache Iceberg enables seamless integration between different streaming and processing engines while maintaining data integrity between them.

Metadata 90

Metadata Data Architecture BI Machine Learning 90

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

Databand.ai

AUGUST 30, 2023

These experts will need to combine their expertise in data processing, storage, transformation, modeling, visualization, and machine learning algorithms, working together on a unified platform or toolset.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Ascend.io

NOVEMBER 28, 2023

Data orchestration refers to a wide collection of methods and tools that coordinate any and all types of data-related computing tasks. These components must work in harmony, driven by a defined operational logic that dictates the activation, response, and contribution of each segment to the collective goal of data processing.

Data Pipeline 52

Data Pipeline IT Data Metadata 52

ProjectPro

FEBRUARY 16, 2023

Data integration with ETL has evolved from structured data stores with high computing costs to natural state storage with read operation alterations thanks to the agility of the cloud. Data integration with ETL has changed in the last three decades.

AWS 52

AWS Data Management ETL Tools Management 52

AltexSoft

NOVEMBER 10, 2021

In a nutshell, the lakehouse system leverages low-cost storage to keep large volumes of data in its raw formats just like data lakes. At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

FEBRUARY 11, 2023

Sample of a high-level data architecture blueprint for Azure BI programs. Source: Pragmatic Works This specialist also oversees the deployment of the proposed framework as well as data migration and data integration processes.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Towards Data Science

MARCH 5, 2024

The shift towards de-normalization In the realm of database design and management, data normalization and de-normalization are fundamental concepts aimed at optimizing data structures for efficient storage, retrieval, and manipulation. Master data operations for cost-effectiveness Employ CREATE TABLE .

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Bytes Google Cloud Cloud Storage Utilities 70

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

Ascend.io

AUGUST 16, 2023

It is awesome to have a third-party group like the ESG validate that through the voice of our own customers,” said Tom Weeks, Chief Operations Officer at Ascend, “Our platform is designed to help data teams achieve leverage by automating the mundane tasks required to create cutting-edge data products.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

ProjectPro

JANUARY 24, 2023

AWS Glue based on several aspects to help you choose the right platform for your big data project needs. What is Azure Data Factory? Azure Data Factory is a cloud-based data integration tool that lets you build data-driven processes in the cloud to orchestrate and automate data transfer and transformation.

AWS 52

AWS Cloud Amazon Web Services ETL Tools 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 82

Data Warehouse Database-centric Metadata Cloud 82

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Data Lake A storage repository where data is stored in its raw format.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Ascend.io

FEBRUARY 15, 2024

It’s not just about handling the data; it’s about doing so in a way that balances resource use with performance, ensuring both cost-effectiveness and data integrity. Ascend collects metadata about these partitions and only processes the downstream components when new data arrives.

Datasets 52

Datasets Database Data Warehouse Data 52

Afterpay Tech

SEPTEMBER 6, 2022

With specific sensitive data handling and data integrity requirements, the Kafka standard libraries couldn’t be used out-of-the-box as we needed to customise them. That eliminated the AWS Transit Gateway option for networking. How did we solve it?

Kafka 52

Kafka AWS Metadata Data Warehouse 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

Cloudera

NOVEMBER 22, 2021

Rise in polyglot data movement because of the explosion in data availability and the increased need for complex data transformations (due to, e.g., different data formats used by different processing frameworks or proprietary applications). As a result, alternative data integration technologies (e.g.,

Government 92

Government Hadoop Data Security Data Warehouse 92