Data Engineering Podcast

APRIL 22, 2018

For this reason metadata management systems are built to track the journey of your business data to aid in analysis, presentation, and compliance. These systems are frequently cumbersome and difficult to maintain, so Octopai was founded to alleviate that burden. Can you talk through the architecture of your platform?

Business Intelligence 113

Business Intelligence Metadata Management Data Governance 113

Data Engineering Podcast

DECEMBER 19, 2021

The team at Metaphor are building a fully connected metadata layer to provide both technical and social intelligence about your data. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL.

Systems 100

Systems Building Metadata Data Warehouse 100

Monte Carlo

FEBRUARY 21, 2023

Enter data fabric: a data management architecture designed to serve the needs of the business, not just those of data engineers. A data fabric is an architecture and associated data products that provide consistent capabilities across a variety of endpoints spanning multiple cloud environments. Table of Contents What is a data fabric?

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

Enter data fabric: a data management architecture designed to serve the needs of the business, not just those of data engineers. A data fabric is an architecture and associated data products that provide consistent capabilities across a variety of endpoints spanning multiple cloud environments. Table of Contents What is a data fabric?

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Cloudera

SEPTEMBER 29, 2022

They are being bombarded with literature about seemingly independent new trends like data mesh and data fabric while dealing with the reality of having to work with hybrid architectures. Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. Miro: [link].

Architecture 73

Architecture Data Architecture Metadata Data Warehouse 73

Data Engineering Podcast

FEBRUARY 20, 2022

You can observe your pipelines with built in metadata search and column level lineage. Your host is Tobias Macey and today I’m interviewing Guy Yachdav, Director of Software Engineering at Immunai, about his work at Immunai to wrangle biological data for advancing research into the human immune system. regulatory, security, etc.)

Systems 100

Systems Software Engineer Software Engineering Data Warehouse 100

Netflix Tech

SEPTEMBER 29, 2022

Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support for Non-Parallelizable Workloads by Kostas Christidis Introduction Timestone is a high-throughput, low-latency priority queueing system we built in-house to support the needs of Cosmos , our media encoding platform. Over the past 2.5

Systems 85

Systems Metadata Media Kafka 85

Databand.ai

AUGUST 30, 2023

DataOps Architecture: 5 Key Components and How to Get Started Ryan Yackel August 30, 2023 What Is DataOps Architecture? A DataOps architecture is the structural foundation that supports the implementation of DataOps principles within an organization. As a result, they can be slow, inefficient, and prone to errors.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Data Engineering Podcast

AUGUST 13, 2022

The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Sifflet also offers a 2-week free trial.

Metadata 100

Metadata MongoDB Scala MySQL 100

AltexSoft

AUGUST 29, 2023

This article explains what a data lake is, its architecture, and diverse use cases. Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. An example of a data lake architecture. Who needs a data lake?

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

DECEMBER 20, 2023

So what is the optimal data observability architecture or data quality coverage strategy? Data observability adds the ability to monitor metadata and logs from the data systems across your stack for full visibility into anomalous behavior. Monitoring for bad data is like fishing. And, even in the cloud, you can’t be everywhere.

Architecture 40

Architecture Data Datasets Machine Learning 40

Cloudera

JULY 15, 2021

The release of Cloudera Data Platform (CDP) Private Cloud Base edition provides customers with a next generation hybrid cloud architecture. All three will be quorums of Zookeepers and HDFS Journal nodes to track changes to HDFS Metadata stored on the Namenodes. Operating System Disk Layouts. Networking .

Architecture 97

Architecture Cloud Kafka Hadoop 97

Rockset

APRIL 26, 2023

In this post, we’ll get to the details: how does one design an RTA system? We’ve noticed many common patterns across streaming data architectures and we’ll be sharing a blueprint for three of the most popular: anomaly detection, IoT, and recommendations. Let’s take a look at a simple architecture and important features to consider.

Architecture 52

Architecture Transportation Data Lake Insurance 52

Data Engineering Podcast

JULY 5, 2021

SaaSGlue is a managed service that lets you connect all of your systems, across clouds and physical infrastructure, and spanning all of your programming languages. How does SaasGlue manage metadata propagation throughout the execution graph? How does SaasGlue manage metadata propagation throughout the execution graph?

Systems 100

Systems Management Data Warehouse Programming Language 100

Ascend.io

APRIL 22, 2024

A unified platform that combines a powerful metadata core, an extensible plugin architecture, DataAware automation, and multiple AI Assistants. As data pipelines sprawl across disparate systems, it becomes harder to trace data lineage, track data quality, and quickly pinpoint issues when they arise.

Project 52

Project Metadata Data Pipeline Data Engineering 52

Monte Carlo

APRIL 16, 2022

The challenge with passive ETL or transformations in the warehouse The modern data warehouse architecture creates problems across many layers. There are several problems with an architecture and process that heavily relies on transforming data once it has entered the data warehouse. Image courtesy of Chad Sanderson.

Data Warehouse 52

Data Warehouse Architecture Data Government 52

Netflix Tech

NOVEMBER 14, 2023

Late-arriving data is essentially delayed data due to system retries, network delays, batch processing schedules, system outages, delayed upstream workflows, or reconciliation in source systems. It leverages Iceberg metadata to facilitate processing incremental and batch-based data pipelines. What is late-arriving data?

Data Engineering 105

Data Engineering Data Engineer Engineering Metadata 105

Monte Carlo

MARCH 4, 2024

Table of Contents How Data Observability Works Made For The Lakehouse: Iceberg and Delta Transform in place: Dynamic Tables and Delta Live Tables Why This Matters How Data Observability Works Data observability provides full visibility into your data, systems, and code so you can find and fix bad data quickly.

Metadata 52

Metadata Data Lake Data Warehouse Data Engineering 52

Data Engineering Podcast

JUNE 5, 2022

The team at Skyflow decided that the second best way is to build a storage system dedicated to securely managing your sensitive information and making it easy to integrate with your applications and data systems. Atlan is the metadata hub for your data ecosystem. Who is the target customer for Skyflow (e.g.

Data Security 100

Data Security Metadata MongoDB Scala 100

Data Engineering Podcast

AUGUST 6, 2022

Summary Data mesh is a frequent topic of conversation in the data community, with many debates about how and when to employ this architectural pattern. Atlan is the metadata hub for your data ecosystem. Can you describe the technical elements of that system? How does that activity map to data systems/products?

Metadata 100

Metadata MongoDB MySQL Business Intelligence 100

LinkedIn Engineering

DECEMBER 19, 2023

The historical upgrade system couldn’t adapt to architectural changes like the introduction of an observer namenode (Now handling a massive influx of read requests – 150K QPS – from services such as Trino ), ZKFC auto-failover, HDFS federation , etc.

Big Data 92

Big Data Hadoop Metadata Data 92

Netflix Tech

NOVEMBER 13, 2023

In this blog post, we will introduce speech and music detection as an enabling technology for a variety of audio applications in Film & TV, as well as introduce our speech and music activity detection (SMAD) system which we recently published as a journal article in EURASIP Journal on Audio, Speech, and Music Processing.

Datasets 89

Datasets Metadata Algorithm Architecture 89

Engineering at Meta

MARCH 6, 2024

Our technical solution builds on Meta’s existing client / server architecture We think the best way to deliver interoperability is through a solution which builds on Meta’s existing client / server architecture [Figure 1]. WhatsApp’s chat protocol uses optimized XML stanzas to communicate with our servers.

Media 132

Media Architecture Metadata Data Storage 132

Data Engineering Podcast

JULY 10, 2022

They also discuss how they have established a guild system for training and supporting data professionals in the organization. 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.

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

Data Engineering Podcast

OCTOBER 16, 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. What are some of the ways that Pathway can be integrated into an analytical system? Atlan is the metadata hub for your data ecosystem.

Database 100

Database Metadata MongoDB MySQL 100

Workfall

JUNE 12, 2023

It bridges gaps in data ecosystems, ensuring consistency and synchronisation across systems. Moreover, it facilitates the implementation of microservices architectures and event-driven systems, automating reactions to data changes without manual intervention.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Netflix Tech

MAY 4, 2023

Behind the scenes, a myriad of systems and services are involved in orchestrating the product experience. These backend systems are consistently being evolved and optimized to meet and exceed customer and product expectations. This blog series will examine the tools, techniques, and strategies we have utilized to achieve this goal.

Utilities 135

Utilities Systems Architecture Coding 135

AltexSoft

FEBRUARY 11, 2023

To get a better understanding of a data architect’s role, let’s clear up what data architecture is. Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. Sample of a high-level data architecture blueprint for Azure BI programs. Problem-solving skills.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Cloudera

JUNE 7, 2022

The same study also revealed that 89% of IT decision makers agree that organizations that implement a hybrid architecture as part of its data strategy will gain a competitive advantage. Companies can now capitalize on the value in all their data, by delivering a hybrid data platform for modern data architectures with data anywhere.

IT 108

IT Unstructured Data Data Architecture Government 108

Christophe Blefari

JANUARY 20, 2024

Still, we can have a common ground when mixing software engineering, DevOps principles, Cloud — or on-prem — systems understanding and data literacy. Some people wrote a white paper detailing Machine Learning Operations (MLOps): Overview, Definition, and Architecture in which they write about rols and missions.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Data Engineering Weekly

MARCH 10, 2024

Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects. This culminated in the creation of GenOS, an operating system for developing GenAI-powered features.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Data Engineering Podcast

MAY 22, 2022

Data lake architectures provide the best combination of massive scalability and cost reduction, but they aren’t always the most performant option. Can you describe the architecture of Flyte? Data orchestration engines are a natural point for generating and taking advantage of rich metadata.

Machine Learning 130

Machine Learning Data Engineering Data Engineer Cloud 130

Ascend.io

FEBRUARY 28, 2024

The foundation of any AI system is high-quality data. 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. And crucially, what does the future hold for data engineering in an AI-driven world?

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Data Engineering Weekly

MAY 16, 2023

In the first part of this series, we talked about design patterns for data creation and the pros & cons of each system from the data contract perspective. In the second part, we will focus on architectural patterns to implement data quality from a data contract perspective. Why is Data Quality Expensive?

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

Christophe Blefari

FEBRUARY 3, 2023

Lyft, powering millions of real-time decisions with LyftLearn Serving — Architecture of the decentralized system Lyft use to deploy and serve ml models. How we deployed a simple wildlife monitoring system on Google Cloud — Artefact engineering a serverless platform on GCP to do wildlife monitoring.

BI 130

BI Google Cloud SQL Machine Learning 130

Data Engineering Weekly

DECEMBER 29, 2022

Data catalogs are the most expensive data integration systems you never intended to build. Data Catalog as a passive web portal to display metadata requires significant rethinking to adopt modern data workflow, not just adding “modern” in its prefix. There are not many sources to pull the metadata. The modern(?)

Metadata 62

Metadata Data Warehouse ETL Tools Data Workflow 62

Knowledge Hut

MAY 9, 2024

Data science is an interdisciplinary academic domain that utilizes scientific methods, scientific computing, statistics, algorithms, processes, and systems to extrapolate or extract knowledge and insights from unstructured, structured, and noisy data. What is Data Science? The standard salary range, however, is $95,000 to $154,000.

Data Science 52

Data Science Data Mining Data Architect Programming Language 52