InfoQ Homepage Big Data Content on InfoQ
-
Grammarly Replaces its in-House Data Lake with Databricks Platform Using Medallion Architecture
Grammarly adopted the medallion architecture while migrating from their in-house data lake, storing Parquet files in AWS S3, to the Delta Lake lakehouse. The company created a new event store for over 6000 event types from 40 internal and external clients and, in the process, improved data quality and reduced the data-delivery time by 94%.
-
How LinkedIn Serves over 4.8 Million Member Profiles per Second
LinkedIn introduced Couchbase as a centralized caching tier for scaling member profile reads to handle increasing traffic that has outgrown their existing database cluster. The new solution achieved over 99% hit rate, helped reduce tail latencies by more than 60% and costs by 10% annually.
-
Discord Migrates Trillions of Messages from Cassandra to ScyllaDB
Discord has migrated trillions of message records from Apache Cassandra to ScyllaDB, reducing the size of the largest cluster from 177 Cassandra nodes to 72 ScyllaDB nodes and reducing tail latencies for reads and writes. The move has unlocked new product use cases because of the improved database stability and performance.
-
Adopting Artificial Intelligence: Things Leaders Need to Know
Artificial intelligence (AI) can help companies identify new opportunities and products, and stay ahead of the competition. Senior software managers should understand the basics of how this new technology works, why agility is important in developing AI products, and how to hire or train people for new roles.
-
AWS Introduces Athena Provisioned Capacity
AWS recently announced a new feature Provisioned Capacity for Athena, that allows users to run SQL queries on fully-managed compute capacity for a fixed price and no long-term commitments.
-
Apache Linkis Graduated to Apache Top-Level Project
Apache Linkis is a computation middleware that acts as a layer between upper-level applications and underlying engines, such as Apache Spark, Apache Hive and Apache Flink. It started as an Apache Incubator project in 2021 and graduated to a Top Level Project in January 2023.
-
Apache Druid 25.0 Delivers Multi-Stage Query Engine and Kubernetes Task Management
Apache Druid is a high-performance real-time datastore and its latest release, version 25.0, provides many improvements and enhancements. The main new features are: the multi-stage query (MSQ) task engine used for SQL-based ingestion is now production ready, and Kubernetes can be used to launch and manage tasks eliminating the need for middle managers...
-
How Twitter Automated Data Quality Check Process
Twitter engineering has recently shared a blog post on how they architected and developed a quality automation platform. Twitter digests and creates thousands of data sets for different data products and applications. The next natural step is to make sure of the quality of the data by adding automation on top of it. In this news post, we explore this architecture in more detail.
-
Uber Freight Near-Real-Time Analytics Architecture
Uber Freight is the Uber platform dedicated to connecting shippers with carriers. Providing reliable service to shippers is crucial for Uber Freight. This is why the Carrier Scorecard was developed, with several metrics including on-time pickup/delivery, tracking automation, and late cancellations.
-
Snap Way to Design Ads Ranking Service Using Deep Learning
Snap engineering has recently published a blog post on how they designed their ads ranking and targeting service using deep learning. Showing ads to the users is the mainstream of social network platform monetization. Snap ad ranking system is designed to target the right user at the right time. Snap is providing an excellent user experience while preserving user privacy and security.
-
Azure Data Explorer Supports Native Ingestion from Amazon S3
Microsoft recently announced the ability to natively ingest data from Amazon S3 into Azure Data Explorer (ADX). The new feature simplifies multi-cloud data analytics deployments, bringing data from Amazon S3 to Azure, without relying on custom ETL pipelines.
-
Next Generation of Data Movement and Processing Platform at Netflix
Netflix engineering recently published in a tech blog how they used data mesh architecture and principles as the next generation of data platform and processing to unleash more business use cases and opportunities. Data mesh is the new paradigm shift in data management that enables users to easily import and use data without transporting it to a centralized location like a data lake.
-
Google Introduces Zero-ETL Approach to Analytics on Bigtable Data Using BigQuery
Recently, Google announced the general availability of Bigtable federated queries, with BigQuery allowing customers to query data residing in Bigtable via BigQuery faster. Moreover, the querying is without moving or copying the data in all Google Cloud regions with increased federated query concurrency limits, closing the longstanding gap between operational data and analytics.
-
Amazon Redshift Serverless Generally Available to Automatically Scale Data Warehouse
Amazon recently announced the general availability of Redshift Serverless, an elastic option to scale data warehouse capacity. The new service allows data analysts, developers and data scientists to run and scale analytics without provisioning and managing data warehouse clusters.
-
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.