Events, Metadata and Raw Data - Data Engineering Digest

1. Streamlining Membership Data Engineering at Netflix with Psyberg

Netflix Tech

NOVEMBER 14, 2023

Types of late-arriving data Based on the structure of our upstream systems, we’ve classified late-arriving data into two categories, each named after the timestamps of the updated partition: Ways to process such data Our team previously employed some strategies to manage these scenarios, which often led to unnecessarily reprocessing unchanged data.

Data Engineering

Data Engineering Data Engineer Engineering Metadata

Solving Data Lineage Tracking And Data Discovery At WeWork

Data Engineering Podcast

DECEMBER 16, 2019

The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools.

Metadata

Metadata PostgreSQL Datasets Data Warehouse

Real-time AI: Live Recommendations Using Confluent and Rockset

Rockset

SEPTEMBER 26, 2023

As smart as ChatGPT appears to be, it can’t summarize current events accurately if it was last trained a year ago and not told what’s happening now. Models may need to know about events, computed metrics, and embeddings based on locality.

Metadata

Metadata Kafka Cloud Database

Webinars

Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

How To Get Promoted In Product Management

MORE WEBINARS

Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Watch our video explaining how data engineering works.

Data Lake

Data Lake Architecture IT Amazon Web Services

Mastering the Art of ETL on AWS for Data Management

ProjectPro

FEBRUARY 16, 2023

ETL Architecture on AWS: Examining the Scalable Architecture for Data Transformation ETL Architecture on AWS typically consists of three components - Source Data Store A Data Transformation Layer Target Data Store Source Data Store The source data store is where raw data is stored before being transformed and loaded into the target data store.

AWS

AWS Data Management ETL Tools Management

Data Engineering Zoomcamp – Data Ingestion (Week 2)

Hepta Analytics

FEBRUARY 14, 2022

When the business intelligence needs change, they can go query the raw data again. ELT: source Data Lake vs Data Warehouse Data lake stores raw data. The purpose of the data is not determined. The data is easily accessible and is easy to update. x+ and set minimum memory to 5GB.

Data Ingestion

Data Ingestion Data Engineering Data Engineer Engineering

How Windward Built Real-Time Logistics Tracking and AI Insights for the Maritime Industry

Rockset

AUGUST 2, 2023

The Windward Maritime AI platform Lastly, Windward wanted to move their entire platform from batch-based data infrastructure to streaming. This transition can support new use cases that require a faster way to analyze events that was not needed until now. They used MongoDB as their metadata store to capture vessel and company data.

Database-centric

Database-centric PostgreSQL Transportation Insurance

Demystifying Modern Data Platforms

Cloudera

SEPTEMBER 15, 2022

July brings summer vacations, holiday gatherings, and for the first time in two years, the return of the Massachusetts Institute of Technology (MIT) Chief Data Officer symposium as an in-person event. A key area of focus for the symposium this year was the design and deployment of modern data platforms.

Data Lake

Data Lake Analytics Application Cloud Storage Architecture

Case Study: Standard Cognition Uses Rockset to Deliver Data APIs and Real-Time Metrics for Vision AI

Rockset

JANUARY 28, 2020

Aside from video data from each camera-equipped store, Standard deals with other data sets such as transactional data, store inventory data that arrive in different formats from different retailers, and metadata derived from the extensive video captured by their cameras.

Retail

Retail Google Cloud Raw Data Data Lake

Building a Data Platform in 2024

Towards Data Science

FEBRUARY 9, 2024

In truth, the synergy between batch and streaming pipelines is essential for tackling the diverse challenges posed to your data platform at scale. The key to seamlessly addressing these challenges lies, unsurprisingly, in data orchestration. This metadata is then utilized to manage, monitor, and foster the growth of the platform.

Building

Building Transportation Data Lake Metadata

AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. Why Use AWS Glue?

AWS

AWS Scala Metadata Data Lake

The Complete Front-End Developer Roadmap 2024

Knowledge Hut

DECEMBER 29, 2023

The “head” tags (<head> and </head>) contain the metadata or information about the website. Not all of the metadata is visible on the website, some of them are information for the browsers. SSG is a tool that generates HTML websites using a set of templates and raw data.

Portfolio

Portfolio Amazon Web Services Programming Language Coding

How I Study Open Source Community Growth with dbt

dbt Developer Hub

NOVEMBER 28, 2021

This could just as easily have been Snowflake or Redshift, but I chose BigQuery because one of my data sources is already there as a public dataset. dbt seeds data from offline sources and performs necessary transformations on data after it's been loaded into BigQuery. Let's dig into each data source one at a time.

Raw Data

Raw Data Metadata Datasets Database

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

SEPTEMBER 21, 2023

The current landscape of Data Observability Tools shows a marked focus on “Data in Place,” leaving a significant gap in the “Data in Use.” ” When monitoring raw data, these tools often excel, offering complete standard data checks that automate much of the data validation process.

Raw Data

Raw Data Data Business Intelligence High Quality Data

Data Engineering Weekly #114

Data Engineering Weekly

JANUARY 15, 2023

SiliconANGLE theCUBE: Analyst Predictions 2023 - The Future of Data Management By far one of the best analyses of trends in Data Management. 2023 predictions from the panel are; Unified metadata becomes kingmaker. The names hold less meaning to the outcome, but its fancy. link] All rights reserved ProtoGrowth Inc, India.

Data Engineering

Data Engineering Data Engineer Engineering Metadata

Link Multiple Data Clouds to Ascend

Ascend.io

FEBRUARY 6, 2023

Data Service – is a group of Data Flows. At this level, users configure team members, connections to other systems, and event notifications. Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products.

Cloud

Cloud Data Ingestion Raw Data Data Pipeline

Link Multiple Data Clouds to Ascend

Ascend.io

FEBRUARY 6, 2023

Data Service – is a group of Data Flows. At this level, users configure team members, connections to other systems, and event notifications. Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products.

Cloud

Cloud Data Ingestion Raw Data Data Pipeline

ELT Process: Key Components, Benefits, and Tools to Build ELT Pipelines

AltexSoft

DECEMBER 23, 2022

For example, Online Analytical Processing (OLAP) systems only allow relational data structures so the data has to be reshaped into the SQL-readable format beforehand. In ELT, raw data is loaded into the destination, and then it receives transformations when it’s needed. ELT allows them to work with the data directly.

Process

Process Building Raw Data Data Lake

Functional Data Engineering — a modern paradigm for batch data processing

Maxime Beauchemin

JANUARY 7, 2018

When functions are “pure” — meaning they do not have side-effects — they can be written, tested, reasoned-about and debugged in isolation, without the need to understand external context or history of events surrounding its execution. This allows for landing immutable blocks of data without delays, in a predictable fashion.

Data Engineering

Data Engineering Data Engineer Data Process Process

Data Vault on Snowflake: Feature Engineering and Business Vault

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse?

Engineering

Engineering Raw Data Data Science Scala

What is dbt Testing? Definition, Best Practices, and More

Monte Carlo

AUGUST 30, 2023

The `dbt run` command will compile and execute your models, thus transforming your raw data into analysis-ready tables. Once the models are created and data transformed, `dbt test` should be executed. This command runs all tests defined in your dbt project against the transformed data. Curious to learn more?

SQL

SQL Datasets Database High Quality Data

Data Orchestration: Defining, Understanding, and Applying

Ascend.io

DECEMBER 11, 2023

Data orchestration is the process of efficiently coordinating the movement and processing of data across multiple, disparate systems and services within a company. Data pipeline orchestration is characterized by a detailed understanding of pipeline events and processes. Not every team needs data orchestration.

Data Workflow

Data Workflow Data Pipeline Data Lake Data

Data Collection for Machine Learning: Steps, Methods, and Best Practices

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more.

Data Collection

Data Collection Machine Learning Unstructured Data Non-relational Database

Get Your Analytics Insights Instantly – Without Abandoning Central IT

Cloudera

JANUARY 21, 2021

With CDW, as an integrated service of CDP, your line of business gets immediate resources needed for faster application launches and expedited data access, all while protecting the company’s multi-year investment in centralized data management, security, and governance. Architecture overview. Separate storage.

IT

IT Data Lake Data Warehouse Cloud Storage

Apache Kafka Architecture and Its Components-The A-Z Guide

ProjectPro

JULY 8, 2021

Kafka Streams and Kafka Connect were used to keep track of the threat of the COVID-19 virus and analyze the data for a more thorough response on local, state, and federal levels. Kafka is an integral part of Netflix’s real-time monitoring and event-processing pipeline. Table of Contents Why is Apache Kafka so popular?

Kafka

Kafka Architecture IT Big Data

Data Lake vs Data Warehouse - Working Together in the Cloud

ProjectPro

AUGUST 11, 2021

The data warehouse layer consists of the relational database management system (RDBMS) that contains the cleaned data and the metadata, which is data about the data. The RDBMS can either be directly accessed from the data warehouse layer or stored in data marts designed for specific enterprise departments.

Data Lake

Data Lake Data Warehouse Cloud Hadoop

How Airbnb Standardized Metric Computation at Scale

Airbnb Tech

JUNE 1, 2021

When a metric is defined in Minerva, authors are required to provide important self-describing metadata. Prior to Minerva, all such metadata often existed only as undocumented institutional knowledge or in chart definitions scattered across various business intelligence tools.

Datasets

Datasets Pipeline-centric Metadata Data Science

What is Data Hub: Purpose, Architecture Patterns, and Existing Solutions Overview

AltexSoft

SEPTEMBER 23, 2021

Data integration layer holds any transformations required to make the data digestible for end users. This often involves such operations as data harmonization, mastering, and enrichment with metadata. Storage layer corresponds to the needs of database management and data modeling. Stambia data hub.

Architecture

Architecture Data Lake Unstructured Data Data Warehouse

Dat: Distributed Versioned Data Sharing with Danielle Robinson and Joe Hand - Episode 16

Data Engineering Podcast

JANUARY 28, 2018

Go to dataengineeringpodcast.com/aicon-new-york to register and save 20% If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th.

Data

Data Project Electronics Data Management

Data Pipeline Architecture Explained: 6 Diagrams and Best Practices

Monte Carlo

JUNE 14, 2023

Airbyte – An open source platform that easily allows you to sync data from applications. Data streaming ingestion solutions include: Apache Kafka – Confluent is the vendor that supports Kafka, the open source event streaming platform to handle streaming analytics and data ingestion.

Data Pipeline

Data Pipeline Architecture Data Lake Data Warehouse

Tutorial: Building An Analytics Data Pipeline In Python

Dataquest

NOVEMBER 4, 2019

As it serves the request, the web server writes a line to a log file on the filesystem that contains some metadata about the client and the request. We store the raw log data to a database. This ensures that if we ever want to run a different analysis, we have access to all of the raw data. PingdomPageSpeed/1.0

Data Pipeline

Data Pipeline Python Building Raw Data

20+ Data Engineering Projects for Beginners with Source Code

ProjectPro

AUGUST 24, 2021

Within no time, most of them are either data scientists already or have set a clear goal to become one. Nevertheless, that is not the only job in the data world. And, out of these professions, this blog will discuss the data engineering job role. This big data project discusses IoT architecture with a sample use case.

Data Engineering

Data Engineering Data Engineer Coding Project

What is ETL Pipeline? Process, Considerations, and Examples

ProjectPro

NOVEMBER 30, 2021

Now that we have understood how much significant role data plays, it opens the way to a set of more questions like How do we acquire or extract raw data from the source? How do we transform this data to get valuable insights from it? Where do we finally store or load the transformed data?

Process

Process Data Pipeline Data Warehouse AWS

Snowflake Architecture and It's Fundamental Concepts

ProjectPro

JANUARY 31, 2022

Provides Powerful Computing Resources for Data Processing Before inputting data into advanced machine learning models and deep learning tools, data scientists require sufficient computing resources to analyze and prepare it. They just need to deliver their data and hand it over to Snowflake to manage.

Architecture

Architecture IT Data Warehouse Amazon Web Services

What is Hadoop 2.0 High Availability?

ProjectPro

MARCH 23, 2015

If any unplanned event triggers, which results in the machine crashing, then the Hadoop cluster would not be available unless the Hadoop Administrator restarts the NameNode. We also use Hadoop and Scribefor log collection, bringing in more than 50TB of raw data per day. What is high availability in Hadoop? With Hadoop 2.0,

Hadoop

Hadoop Big Data Architecture Metadata

Unstructured Data: Examples, Tools, Techniques, and Best Practices

AltexSoft

MAY 12, 2023

Data lakes offer a flexible and cost-effective approach for managing and storing unstructured data, ensuring high durability and availability. Another NLP approach for handling unstructured text data is information extraction (IE). Last but not least, you may need to leverage data labeling if you train models for custom tasks.

Unstructured Data

Unstructured Data NoSQL Hadoop Data Lake

Data Preprocessing - Techniques, Concepts and Steps to Master

ProjectPro

OCTOBER 29, 2021

Since then, many other well-loved terms, such as “data economy,” have come to be widely used by industry experts to describe the influence and importance of big data in today’s society. Accuracy Accuracy refers to how well the information recorded reflects a real event or object.

Data Mining

Data Mining Datasets Machine Learning Metadata

Real-Time Analytics in the World of Virtual Reality and Live Streaming

Rockset

SEPTEMBER 6, 2019

Virtual Reality – The Next Frontier in Media I work as a Data Engineer at a leading company in the VR space, with a mission to capture and transmit reality in perfect fidelity. Our content varies from on-demand experiences to live events like NBA games, comedy shows and music concerts.

Metadata

Metadata Kafka Data Cleanse SQL

Using Metrics Layer to Standardize and Scale Experimentation at DoorDash

DoorDash Engineering

APRIL 12, 2023

As we mentioned in our previous blog , we began with a ‘Bring Your Own SQL’ method, in which data scientists checked in ad-hoc Snowflake (our primary data warehouse) SQL files to create metrics for experiments, and metrics metadata was provided as JSON configs for each experiment.

SQL

SQL Metadata Raw Data Government

Data Contracts and Data Observability: Whatnot’s Full Circle Journey to Data Trust

Monte Carlo

JANUARY 4, 2024

Data processing : Whatnot data teams rely on Snowflake and dbt for processing, with orchestration in Dagster. “All It’s quite dynamic, and analytics events that represent ephemeral things happening in real time are incredibly valuable for us. Data quality challenges at Whatnot And you know what they say: mo’ data, mo’ problems.

Data

Data Metadata Software Engineer Software Engineering

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

AltexSoft

MARCH 14, 2023

Moreover, over 20 percent of surveyed companies were found to be utilizing 1,000 or more data sources to provide data to analytics systems. These sources commonly include databases, SaaS products, and event streams. Databases store key information that powers a company’s product, such as user data and product data.

IT

IT Data Warehouse Data Governance Data Lake

What is a Data Platform? And How to Build An Awesome One

Monte Carlo

AUGUST 19, 2023

Airbyte – An open source platform that easily allows you to sync data from applications. Data streaming ingestion solutions include: Apache Kafka – Confluent is the vendor that supports Kafka, the open source event streaming platform to handle streaming analytics and data ingestion.

Building

Building BI Data Lake Data Governance

1. Streamlining Membership Data Engineering at Netflix with Psyberg

Solving Data Lineage Tracking And Data Discovery At WeWork

Webinars

Trending Sources

Real-time AI: Live Recommendations Using Confluent and Rockset

Webinars

Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

Mastering the Art of ETL on AWS for Data Management

Data Engineering Zoomcamp – Data Ingestion (Week 2)

How Windward Built Real-Time Logistics Tracking and AI Insights for the Maritime Industry

Demystifying Modern Data Platforms

Case Study: Standard Cognition Uses Rockset to Deliver Data APIs and Real-Time Metrics for Vision AI

Building a Data Platform in 2024

AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

The Complete Front-End Developer Roadmap 2024

How I Study Open Source Community Growth with dbt

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

Data Engineering Weekly #114

Link Multiple Data Clouds to Ascend

Link Multiple Data Clouds to Ascend

ELT Process: Key Components, Benefits, and Tools to Build ELT Pipelines

Functional Data Engineering — a modern paradigm for batch data processing

Data Vault on Snowflake: Feature Engineering and Business Vault

What is dbt Testing? Definition, Best Practices, and More

Data Orchestration: Defining, Understanding, and Applying

Data Collection for Machine Learning: Steps, Methods, and Best Practices

Get Your Analytics Insights Instantly – Without Abandoning Central IT

Apache Kafka Architecture and Its Components-The A-Z Guide

Data Lake vs Data Warehouse - Working Together in the Cloud

How Airbnb Standardized Metric Computation at Scale

What is Data Hub: Purpose, Architecture Patterns, and Existing Solutions Overview

Dat: Distributed Versioned Data Sharing with Danielle Robinson and Joe Hand - Episode 16

Data Pipeline Architecture Explained: 6 Diagrams and Best Practices

Tutorial: Building An Analytics Data Pipeline In Python

20+ Data Engineering Projects for Beginners with Source Code

What is ETL Pipeline? Process, Considerations, and Examples

Snowflake Architecture and It's Fundamental Concepts

Top 100 Hadoop Interview Questions and Answers 2023

What is Hadoop 2.0 High Availability?

Unstructured Data: Examples, Tools, Techniques, and Best Practices

Data Preprocessing - Techniques, Concepts and Steps to Master

Real-Time Analytics in the World of Virtual Reality and Live Streaming

Using Metrics Layer to Standardize and Scale Experimentation at DoorDash

Data Contracts and Data Observability: Whatnot’s Full Circle Journey to Data Trust

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

What is a Data Platform? And How to Build An Awesome One

Stay Connected