Towards Data Science

FEBRUARY 6, 2024

ERP and CRM systems are designed and built to fulfil a broad range of business processes and functions. Accessing Operational Data I used to connect to views in transactional databases or APIs offered by operational systems to request the raw data. Does it sound familiar?

Systems 83

Systems Raw Data Metadata Data Cleanse 83

Knowledge Hut

APRIL 25, 2024

Intrusion detection systems (IDS) are designed to identify suspicious and malicious activity through network traffic. So, let's get to know the meaning of an intrusion detection system and how it works. What Is a n Intrusion Detection System? Why Are Intrusion Detection Systems Important? and how it works.

Systems 52

Systems Recruitment Machine Learning Certification 52

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. What is involved in deploying your metadata collection agents?

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

Data Engineering Podcast

SEPTEMBER 11, 2022

Summary Data engineering systems are complex and interconnected with myriad and often opaque chains of dependencies. 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.

Systems 100

Systems Metadata Building MongoDB 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

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.

Systems 100

Systems Software Engineer Software Engineering Data Warehouse 100

Data Engineering Podcast

APRIL 24, 2022

WhyLogs is a powerful library for flexibly instrumenting all of your data systems to understand the entire lifecycle of your data from source to productionized model. You have full control over your data and their plugin system lets you integrate with all of your other data tools, including data warehouses and SaaS platforms.

Machine Learning 100

Machine Learning Systems Data Lake Metadata 100

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. The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Sifflet also offers a 2-week free trial. Sifflet also offers a 2-week free trial.

Metadata 100

Metadata MongoDB Scala MySQL 100

Snowflake

MAY 15, 2024

And that gap is directly affecting the performance and reliability of AI systems everywhere. By continuously monitoring metrics, metadata, lineage, and logs from across your data infrastructure and using ML-based anomaly detection to detect issues, they help data teams know about and resolve issues quickly.

Cloud 106

Cloud Metadata Data Pipeline Government 106

Precisely

MAY 13, 2024

Whether you’re bringing a new system online or connecting an existing database with your analytics platform, the process should be simple and straightforward. Integrated data catalog for metadata support As you build out your IT ecosystem, it’s important to leverage tools that have the capabilities to support forward-looking use cases.

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Booking.com Engineering

DECEMBER 2, 2022

We take advantage of this feature in our ad bidding systems, maintaining consistent data views from our Account Specialists’ spreadsheets, to our Data Scientists’ notebooks, to our bidding system’s in-memory data. Managing Workflows Data, delivered with quality at the required moment, is at the heart of our system performance.

Systems 52

Systems Cloud MySQL Relational Database 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

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

Data Engineering Podcast

NOVEMBER 6, 2022

He also describes the considerations involved in bringing behavioral data into your systems, and the ways that he and the rest of the Snowplow team are working to make that an easy addition to your platforms. Atlan is the metadata hub for your data ecosystem. What are some of the unique characteristics of that information?

Building 130

Building IT Metadata MongoDB 130

Netflix Tech

JUNE 1, 2023

In this blog post, we’ll discuss the methods we used to ensure a successful launch, including: How we tested the system Netflix technologies involved Best practices we developed Realistic Test Traffic Netflix traffic ebbs and flows throughout the day in a sinusoidal pattern. Basic with ads was launched worldwide on November 3rd.

Algorithm 136

Algorithm Metadata Kafka Systems 136

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

Data Engineering Podcast

JULY 31, 2022

Because of its centrality to your data systems it is valuable for debugging, governance, understanding context, and myriad other purposes. 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.

IT 100

IT Metadata MongoDB MySQL 100

Data Engineering Podcast

DECEMBER 4, 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. Why is mobile access to a business intelligence system important? Atlan is the metadata hub for your data ecosystem.

Business Intelligence 100

Business Intelligence Metadata BI MongoDB 100

Data Engineering Podcast

JULY 17, 2022

Whether that data is public, paid, or scraped it requires investment and upkeep to acquire and integrate it with your systems. 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.

Data Management 130

Data Management Management Metadata MongoDB 130

Data Engineering Podcast

OCTOBER 30, 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. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform.

Business Intelligence 130

Business Intelligence Metadata MongoDB MySQL 130

Data Engineering Podcast

JUNE 19, 2022

Summary Data analysis is a valuable exercise that is often out of reach of non-technical users as a result of the complexity of data systems. 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 130

Metadata Unstructured Data MongoDB MySQL 130

Data Engineering Weekly

MARCH 24, 2024

The author goes beyond comparing the tools to various offerings from streaming vendors in stream processing and Kafka protocol-supported systems. The logging engine to debug AI workflow logs is an excellent system design study if you’re interested in it. The extracted key-value pairs are written to the line’s metadata.

Data Engineering 62

Data Engineering Data Engineer Engineering Metadata 62

Data Engineering Weekly

NOVEMBER 11, 2022

I took a break from data engineering and focused on system monitoring and observability for some time. Little did I know I would be building data pipelines to measure system efficiency instead of measuring business efficiency. Not too long ago, I worked for a famous messaging platform; You may be typing on it as you read this blog.

Systems 40

Systems High Quality Data Consulting Metadata 40

Netflix Tech

NOVEMBER 14, 2023

Input : List of source tables and required processing mode Output : Psyberg identifies new events that have occurred since the last high watermark (HWM) and records them in the session metadata table. The session metadata table can then be read to determine the pipeline input. Audit Run various quality checks on the staged data.

Metadata 97

Metadata Data Pipeline Scala Data Workflow 97

Data Engineering Podcast

AUGUST 6, 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. 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

Netflix Tech

DECEMBER 14, 2023

Knowledge Management — Leveraging Institutional Data Tristan Reid, software engineer, shares experiences about the Knowledge Management project at Netflix, which seeks to leverage language modeling techniques and metadata from internal systems to improve the impact of the >100K memos that circulate within the company.

Data Engineering 103

Data Engineering Data Engineer Engineering Metadata 103

Data Engineering Podcast

JULY 3, 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. How do lakehouse/data virtualization systems allow for that? Atlan is the metadata hub for your data ecosystem.

Architecture 100

Architecture Metadata MongoDB MySQL 100

Edureka

MAY 6, 2024

MLOps is an emerging discipline that aims to unify and streamline the machine learning system development (Dev) and operations (Ops) lifecycle. MLOps refers to the practice and discipline within machine learning that aims to unify and streamline machine learning system development (Dev) and operations (Ops).

Machine Learning 52

Machine Learning Metadata Programming Language Healthcare 52

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

Engineering at Meta

SEPTEMBER 7, 2023

Our agile, reproducible, and standardized benchmarking system plays an important role in this. The limitations of traditional AI benchmarking methodology Traditionally, benchmarking AI systems has largely relied on running full ML workloads. Additionally, gauging the performance of a system using this method can be time-consuming.

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Metadata Utilities Systems Designing 103

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

Monte Carlo

APRIL 20, 2024

To do this, you need data monitors that drill “ deep ” into the data using both machine learning and user-defined rules, as well as metadata monitors to scale “ broadly ” across every production table in your environment and to be fully integrated across your stack. Maybe it’s time we embraced data observability instead.

IT 52

IT Metadata Data Warehouse BI 52

Knowledge Hut

APRIL 4, 2024

Chef Solo) Users write system configuration files that are called ‘Recipes’, which are then organized into ‘Cookbooks’. Nodes, or the systems needing the configuration, are the final part. Keeping the Chef repository in sync with the source code revision control system. Validating Chef's source code. Nodes can be of any type, i.e:

Architecture 52

Architecture Amazon Web Services Metadata AWS 52

Monte Carlo

JANUARY 5, 2024

In this article, we’ll peel back the 5 layers that make up data lakehouse architecture: data ingestion, data storage, metadata, API, and data consumption, understand the expanded opportunities a data lakehouse opens up for generative AI, and how to maintain data quality throughout the pipeline with data observability. Metadata layer 4.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

In this article, we’ll peel back the 5 layers that make up data lakehouse architecture: data ingestion, data storage, metadata, API, and data consumption, understand the expanded opportunities a data lakehouse opens up for generative AI, and how to maintain data quality throughout the pipeline with data observability. Metadata layer 4.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

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