Lyft Engineering

JUNE 28, 2023

The Event Driven Decisions capability in particular turned out to be general enough as to be applicable to a wide range of use cases. At the time of writing, a Mapping team is working to utilize theEvent Driven Decisions product to rebuild Lyft’s Traffic infrastructure by aggregating data per geohash and applying a model.

Machine Learning 116

Machine Learning Building Metadata Kafka 116

Rockset

AUGUST 30, 2021

Apache Kafka has made acquiring real-time data more mainstream, but only a small sliver are turning batch analytics, run nightly, into real-time analytical dashboards with alerts and automatic anomaly detection. But until this release, all these data sources involved indexing the incoming raw data on a record by record basis.

SQL 52

SQL Kafka MongoDB MySQL 52

Confluent

FEBRUARY 6, 2019

The blog posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ® ecosystem as a central, scalable and mission-critical nervous system. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Python Kafka Java 107

ProjectPro

JUNE 28, 2016

Apache Kafka is breaking barriers and eliminating the slow batch processing method that is used by Hadoop. This is just one of the reasons why Apache Kafka was developed in LinkedIn. Kafka was mainly developed to make working with Hadoop easier. This data is constantly changing, and is voluminous.

Kafka 40

Kafka Systems Hadoop Big Data 40

Lyft Engineering

SEPTEMBER 27, 2022

The very first version (see Figure 1) was designed to consume events, convert data to ML features, orchestrate model executions, and sync decision variables to their respective services. This pipeline ingests tens of millions of events per second and processes them into machine learning features.

Kafka 52

Kafka Aggregated Data Machine Learning Architecture 52

Rockset

FEBRUARY 16, 2024

Furthermore, Rockset’s ability to pre-aggregate data at ingestion time reduced the cost of storage and sped up queries, making the solution cost-effective at scale. With Rockset’s flexible data model , the team could easily define new metrics, add new data and onboard customers without significant engineering resources.

Architecture 59

Architecture SQL Data Warehouse Database 59

LinkedIn Engineering

DECEMBER 19, 2023

Our RU framework ensures that our big data infrastructure, which consists of over 55,000 hosts and 20 clusters holding exabytes of data, is deployed and updated smoothly by minimizing downtime and avoiding performance degradation. We needed a deep understanding of system dependencies to ensure a smooth deployment process.

Big Data 92

Big Data Hadoop Metadata Data 92

Netflix Tech

JULY 12, 2019

Gatekeeper accomplishes its prescribed task by aggregating data from multiple upstream systems, applying some business logic, then producing an output detailing the status of each video in each country. and can achieve orders of magnitude more efficient data access, which opens up many possibilities.

Datasets 73

Datasets Kafka Architecture Computer Science 73

LinkedIn Engineering

DECEMBER 20, 2023

The feedback loop serves as a critical component of a dynamic defense strategy, constantly monitoring and aggregating data from abuse reports, member feedback, and reviewer input. By scrutinizing patterns with abuse data, we pinpoint emerging trends, allowing us to fine-tune our models and systems in real-time.

Building 91

Building Algorithm Kafka Machine Learning 91

ProjectPro

DECEMBER 7, 2021

Data pipelines must be scalable due to the volume of big data, which might fluctuate over time. The big data pipeline must process data in large volumes concurrently because, in reality, multiple big data events are likely to occur at once or relatively close together.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

JANUARY 25, 2022

Features of PySpark Features that contribute to PySpark's immense popularity in the industry- Real-Time Computations PySpark emphasizes in-memory processing, which allows it to perform real-time computations on huge volumes of data. PySpark is used to process real-time data with Kafka and Streaming, and this exhibits low latency.

Big Data 52

Big Data Data Process Process Kafka 52

Rockset

NOVEMBER 1, 2022

Step 1: Data Acquisition Elasticsearch is rarely the system of record which means the data in it comes from somewhere else for real-time analytics. Rockset has built-in connectors to stream real-time data for testing and simulating production workloads including Apache Kafka , Kinesis and Event Hubs.

Database-centric 52

Database-centric Pipeline-centric SQL Aggregated Data 52

Ascend.io

SEPTEMBER 14, 2023

Use Case: Storing data with PostgreSQL (example) import psycopg2 conn = psycopg2.connect(dbname="mydb", Tailored libraries like PySpark Streaming and Kafka-Python have made real-time data analysis and event processing a streamlined affair in Python. csv') data_excel = pd.read_excel('data2.xlsx')

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This architecture shows that simulated sensor data is ingested from MQTT to Kafka.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

NOVEMBER 15, 2021

It serves as a distributed processing engine for both categories of data streams: unbounded and bounded. Support for stream and batch processing, comprehensive state management, event-time processing semantics, and consistency guarantee for the state are just a few of Flink's capabilities. CMAK is developed to help the Kafka community.

Big Data 52

Big Data Project Metadata Programming Language 52

Rockset

FEBRUARY 25, 2021

Rockset, on the other hand, provides full-featured SQL and an API endpoint interface that allows developers to quickly join across data sources like DynamoDB and Kafka. Joins are often used in real-time analytics applications to combine streaming data (usually representing events) with static data (like customer information).

SQL 40

SQL Data Pipeline Kafka Database 40

Knowledge Hut

NOVEMBER 17, 2023

To be an Azure Data Engineer, you must have a working knowledge of SQL (Structured Query Language), which is used to extract and manipulate data from relational databases. You should be able to create intricate queries that use subqueries, join numerous tables, and aggregate data.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

AltexSoft

SEPTEMBER 21, 2023

Analysis of logs, metrics, and security events. With Elasticsearch, you can aggregate and analyze large streams of logs, metrics, and security events in near real-time, making it indispensable for system monitoring and security information and event management (SIEM). Real-time behavior modeling with ML.

Engineering 52

Engineering NoSQL Programming Language Java 52

phData: Data Engineering

JANUARY 3, 2022

This likely requires you to aggregate data from your ERP system, your supply chain system, potentially third-party vendors, and data around your internal business structure. Once the data has been collected from each system, a data engineer can determine how to optimally join the data sets.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

DoorDash Engineering

OCTOBER 17, 2023

Experiment exposures are one of our highest volume events. On a typical day, our platform produces between 80 billion and 110 billion exposure events. We stream these events to Kafka and then store them in Snowflake. Users can query this data to troubleshoot their experiments. For this we used Apache Pinot.

Education 87

Education Kafka Algorithm Data Warehouse 87

ProjectPro

JULY 27, 2021

It involves creating a visual representation of an entire system of data or a part of it. The process of data modeling begins with stakeholders providing business requirements to the data engineering team. Data warehouse Operational database Data warehouses generally support high-volume analytical data processing - OLAP.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Rockset

APRIL 15, 2022

It’s probably because their analytics database lacks the features necessary to deliver data-driven decisions accurately in real time. It’s probably because their analytics database lacks the features necessary to deliver data-driven decisions accurately in real time. Transmitting out-of-order data is not the issue.

Analytics Application 52

Analytics Application Data Warehouse Kafka Raw Data 52

AltexSoft

MARCH 14, 2023

Additionally, this modularity can help prevent vendor lock-in, giving organizations more flexibility and control over their data stack. Many components of a modern data stack (such as Apache Airflow, Kafka, Spark, and others) are open-source and free. These sources commonly include databases, SaaS products, and event streams.

IT 59

IT Data Warehouse Data Governance Data Lake 59