InfoQ Homepage Data Pipelines Content on InfoQ
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Confluent Announces Apache Flink on Confluent Cloud in Open Preview
Confluent recently announced the open preview of Apache Flink on Confluent Cloud as a fully-managed service for stream processing. The company claims that the managed service will make it easier for companies to filter, join, and enrich data streams with Flink.
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Running Apache Flink Applications on AWS KDA: Lessons Learnt at Deliveroo
Deliveroo introduced Apache Flink into its technology stack for enriching and merging events consumed from Apache Kafka or Kinesis Streams. The company opted to use AWS Kinesis Data Analytics (KDA) service to manage Apache Flink clusters on AWS and shared its experiences from running Flink applications on KDA.
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Pfizer Uses Serverless Architecture on AWS to Scale Processing of Digital Biomarkers
Pfizer upgraded the serverless architecture for processing digital biomarker data at scale to make it more flexible and configurable. They created a framework that uses a file processing pipeline built with AWS Step Functions and other serverless services, as well as a custom Python package for data ingestion and processing.
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Yelp Rebuilds Corrupted Cassandra Cluster Using Its Data Streaming Architecture
Yelp created a solution to sanitize data from the corrupted Apache Cassandra cluster utilizing its data streaming architecture. The team explored many potential options to address the data corruption issue, however, ultimately had to move the data into a new cluster to remove corrupted records in the process.
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Instacart Creates a Self-Serve Apache Flink Platform on Kubernetes
Instacart moved their Apache Flink workloads from AWS EMR to Kubernetes to meet the high demand for data processing use cases using Flink within the organization, as using EMR became problematic for many teams with different requirements. As a result, they made the platform easier to use and reduced their operational and infrastructure costs.
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Strategies and Principles to Scale and Evolve MLOps - at QCon London
At the QCon London conference, Hien Luu, senior engineering manager for the Machine Learning Platform at DoorDash, discussed strategies and principles for scaling and evolving MLOps. With 85% of ML projects failing, understanding MLOps at an engineering level is crucial. Luu shared three core principles: "Dream Big, Start Small," "1% Better Every Day," and "Customer Obsession."
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AWS Publishes Reference Architecture and Implementations for Deployment Pipelines
AWS recently released a reference architecture and a set of reference implementations for deployment pipelines. The recommended architectural patterns are based on best practices and lessons collected at Amazon and customer projects.
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AWS Glue Now Supports Crawler History
AWS recently launched support for histories of AWS Glue Crawlers, which allows the interrogation of Crawler executions and associated schema changes for the last 12 months.
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Shopify’s Practical Guidelines from Running Airflow for ML and Data Workflows at Scale
Shopify engineering shared its experience in the company's blog post on how to scale and optimize Apache Airflow for running ML and data workflows. They shared practical solutions for the challenges they faced like slow file access, insufficient control over DAG, irregular level of traffic, resource contention among workloads, and more.
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Data Collection, Standardization and Usage at Scale in the Uber Rider App
Uber Engineering recently published how it collects, standardises and uses data from the Uber Rider app. Rider data comprises all the rider's interactions with the Uber app. This data accounts for billions of events from Uber's online systems every day. Uber uses this data to deal with top problem areas such as increasing funnel conversion, user engagement, etc.
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QCon Plus November 2021 is Now Hybrid. Attend Online and In-Person (NY & SF)
The QCon Plus software development conference will be back November 1-5, 2021 - online and in-person. Get the chance to engage and network with professionals driving change and innovation inside the world’s most innovative software organizations.
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Airbnb Builds Himeji - a Scalable Centralized Authorization System
Airbnb recently described how it built Himeji, a scalable centralized authorization system. Himeji stores permissions data and performs permission checks as a central source of truth. It uses a sharded and replicated in-memory cache to improve performance and lower latencies and has served checks in production for about a year.
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Designing for Failure in the BBC's Analytics Platform
Last week at InfoQ Live, Blanca Garcia-Gil, principal systems engineer at BBC, gave a session on Evolving Analytics in the Data Platform. During this session, Garcia-Gil focused on how her team prepared and designed for two types of failure - "known unknowns" and "unknown unknowns."
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PayPal Standardizes on Apache Airflow and Apache Gobblin for Its Next-Gen Data Movement Platform
PayPal recently described how it standardized on Apache Airflow and Apache Gobblin for implementing its next-gen data movement platform. In a recent blog post, PayPal engineers detail how the existing data movement platform evolved into many tools & platforms in a complex and unmanageable ecosystem and their shift towards a new implementation.
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Data Mesh Principles and Logical Architecture Defined
The concept of a data mesh provides new ways to address common problems around managing data at scale. Zhamak Dehghani has provided additional clarity around the four principles of a data mesh, with a corresponding logical architecture and organizational structure.