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Data Pipelines
65 posts about Data Pipelines. Every summary links to the original.
Airbnb ·
Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize
Airbnb has open-sourced project-lighthouse-anonymize, a Python library powering their privacy-preserving anonymization used to measure user experience disparities. The release is accompanied by technical papers detailing Core Mondrian, an extended partition-based anonymization algorithm, alongside a framework for measuring anonymized data quality. The library enforces k-anonymity and p-sensitive k-anonymity while offering built-in data quality metrics.
Adam BloomstonHow Standardizing Product Telemetry Reduced Time to Insight by 97%
Salesforce faced scaling bottlenecks when individual engineering teams maintained fragmented, custom telemetry pipelines requiring manual data transformations and dashboard creation every release. To resolve these silos, the engineering organization established the Product Data Platform (PDP) using a standardized custom schema built atop Monitoring Cloud infrastructure. This framework mandates core telemetry fields for consistent metric generation while offering governed optional attributes for product-specific flexibility. An AI-driven Model Context Protocol tool assists developers by recommending compliant instrumentation patterns directly within their workflows. Today, the unified platform processes 45 billion rows of data daily across 19,000 distinct events and 2,000 product features, reducing dashboard refresh latency by 97 percent from one month to daily updates.
Scott NybergGrab ·
How AI is transforming analytics at Grab
Grab is restructuring its analytics operations using a five-level AI autonomy ladder, transitioning analysts from manual artifact creation to problem framing and decision governance. The architecture leverages domain-specific systems like Spartan to process natural language queries through certified metric indexes and Scarlet to triage and repair failing data pipelines. To prevent agent hallucinations, ContextIQ manages context lifecycles by automatically updating metric definitions, SQL references, and golden-dataset test cases when instrumentation changes or failures occur. Furthermore, data teams use an internal portal called BriX to configure custom analytics surfaces and automated root-cause analysis commentaries using reusable Model Context Protocol connections. Autonomy scales mechanical query and validation tasks while keeping human oversight focused on canonical metric definitions and strategic sign-offs.
Maanas PrabhakarNetflix ·
Modeling Device Capabilities for Analytics
Netflix supports diverse features, such as 4K streaming, immersive audio, and cloud gaming, across varied hardware platforms. Because hardware constraints like RAM, CPU cores, and display capabilities limit feature support on certain models, understanding device capabilities is crucial for managing user experiences. To address this challenge, Netflix built a comprehensive device capability data model integrated with internal feature flags. The architecture employs a cumulative table to capture the latest state of device capabilities alongside a histogram table tracking 28-day active device counts grouped by model and software version. These datasets power analytical products that evaluate feature reach and inform rollout decisions for capabilities like Netflix Spatial Audio and 4K Ultra HD.
Netflix Technology BlogDropbox ·
How our universal content processing platform Riviera evolved for AI and beyond
Dropbox developed Riviera, a centralized content processing platform that evolved from an internal file preview generator into a shared infrastructure supporting over 300 file formats. By decomposing complex conversion tasks into modular, reusable transformations, the system now powers search indexing, media transcoding, and AI context extraction across Dropbox products.
Ilya Yakovlev,Andrew Cheung,Binoy DashGrab ·
Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption
Grab transitioned its petabyte-scale data lake from Hive Parquet to Apache Iceberg to resolve metadata bottlenecks, small file fragmentation, and lack of native ACID support. Implementing Iceberg with Z-ordering reduced navigation query runtimes from 70 seconds to 6 seconds and cut daily S3 API costs by up to 95% on key operational datasets. To seamlessly manage concurrent table formats without forcing engineers to specify format prefixes, Grab developed and open-sourced UnifiedSparkCatalog. This custom catalog transparently detects table types via the Hive Metastore, routes queries to format-specific handlers, and retains backwards compatibility for legacy Hive operations.
Rahul PentiNetflix ·
The Data Canary: How Netflix Validates Catalog Metadata
A manual mitigation action during an incident corrupted a data feed for a subset of titles, causing playback issues and catalog service failures that existing code canary systems failed to catch. To protect streaming reliability, Netflix built an automated data canary system that validates transformed catalog metadata prior to publication. The architecture utilizes a dedicated orchestrator alongside permanent baseline and canary service clusters to coordinate validation using real production traffic. By leveraging custom chaos experiment thresholds, sticky session affinity, and Starts Per Second playback metrics, the system detects regressions in under ten minutes and blocks publication automatically. Controlled failure injection experiments routing approximately 0.2% of global traffic confirmed that issues could be identified in 2.5 to 4 minutes.
Netflix Technology BlogAirbnb ·
Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world
To support its expansion from Homes into Experiences and Services, Airbnb evolved its offline data warehouse architecture to balance domain-specific needs with organizational consistency. Rather than enforcing a single global pattern, data teams adopted a framework of foundational principles that paired separate data models for unique product features with monolithic models for cross-cutting domains like payments and messaging.
Patrick LamAirbnb ·
When history fails you, borrow from geography
When historical travel data became uninformative during the asynchronous post-COVID recovery, Airbnb redesigned its demand forecasting to borrow signals across geographies rather than waiting for local data. Using a hierarchical Bayesian framework, the team propagated updated posterior estimates from early-recovering corridors as informative priors for structurally similar, later-recovering corridors. This allowed accurate corridor-level demand forecasting in near real time during periods of high disruption and uneven market reopening.
Harrison KatzGrab ·
The Hugo evolution: Engineering Grab's unified, one-click data ingestion platform with Apache Flink
Grab's self-service data platform, Hugo, faced significant onboarding friction as streaming pipelines expanded across fragmented systems like Kafka Connect, custom Go applications, and Spark. Engineering teams struggled with cross-platform configuration translations and brittle, manual schema mappings that stretched onboarding over several days. To resolve these bottlenecks, Grab modernized the ingestion architecture by introducing a centralized automation layer powered by Apache Flink and Flink CDC. The updated platform dynamically retrieves Protobuf schemas from Confluent Schema Registry and ingests MySQL binlogs directly into queryable Hive tables without intermediate Kafka hops. This shift dropped pipeline onboarding times to roughly six minutes for Kafka and three minutes for MySQL CDC, driving more pipeline adoptions in one year than in the previous five.
Shuguang XiangSlack ·
From SSH to REST: A Security-Driven Modernization of Slack’s EMR Data Pipelines
Slack's data platform accumulated over 700 SSH-based Airflow operators executing jobs directly on AWS Elastic MapReduce clusters, creating broad security surfaces and operational instability. Stateful SSH connections frequently failed when Kubernetes pods restarted, leaving orphaned zombie processes and preventing infrastructure modernization such as migrating EMR clusters to child accounts. To eliminate SSH access, Slack adopted a REST-based architecture using Quarry, a gateway providing unified REST APIs and token authentication across compute engines. For non-Hadoop CLI workloads, Slack leveraged YARN Distributed Shell to execute arbitrary shell scripts stored in Amazon S3 within managed, isolated YARN containers. The phased migration achieved zero downtime across eight data regions, unlocking reliable monitoring, simplified onboarding, and future initiatives like Spark on Kubernetes.
Mahendran VasagamGrab ·
Data Mesh at Grab (Part II): The foundational tools behind certification
Grab operationalizes its Signals Marketplace data mesh through integrated platforms designed for continuous data certification and observability. The central metadata management platform, Hubble, extends open-source DataHub to model metadata as an event-driven graph and expose search, lineage, ownership, and data contracts. An automated certification engine built on the DataHub Actions framework continuously evaluates metadata changes, classifying assets into Uncertified, Certified, CertifiedPlus, or Revoked states. Genchi serves as the data quality observability layer, using Temporal and Kafka to run checks for freshness, volume completeness, schema stability, and semantic rules. To eliminate false-positive alerts caused by decoupled cron schedules, Genchi integrates with the Lighthouse monitoring service to trigger quality tests immediately upon pipeline completion.
Aezo TeoGrab ·
From firefighting to building: How AI agents restored our team’s core productivity
Grab's Analytics Data Warehouse team spent roughly 40% of their engineering bandwidth answering repetitive questions, tracing data lineage, and handling basic pipeline enhancement requests across more than 15,000 tables. To eliminate these manual investigative bottlenecks, the team implemented a multi-agent AI architecture using FastAPI, LangGraph, Redis, and PostgreSQL. Incoming requests route through two dedicated pathways: an enhancement pipeline for generating code changes and an investigation pipeline for diagnosing data anomalies. Specialized agents interact with underlying engines like Trino, GitLab, and observability platforms to query data, trace transformations, and check ongoing incidents before synthesizing findings. This system automates the context-gathering process within minutes while maintaining human-in-the-loop review for merge requests and production changes.
Sneh AgrawalGrab ·
How Grab is accelerating growth with real-time personalization using Customer Data Platform scenarios
Grab previously relied on daily batch attribute updates in its Customer Data Platform, which created engineering bottlenecks and hindered time-sensitive engagement opportunities. To address this limitation, the team introduced Scenarios, a self-serve real-time personalization capability embedded within the platform. The architecture processes real-time event triggers from Grab's Scribe platform using Apache Flink, enriches incoming events with historical context from StarRocks, and evaluates pre-trained machine learning classifiers. Computed outputs sync to Kafka streams or Amphawa, an internal feature store powered by AWS DynamoDB, maintaining end-to-end latencies under fifteen seconds. Across more than a dozen production deployments, including real-time interventions for subscription abandonment within fifteen minutes, the platform achieved over a 3% conversion uplift compared to batch campaigns.
Saubhagya AwaneeshGrab ·
Real-time data quality monitoring: Kafka stream contracts with syntactic and semantic test
Kafka streams often suffer from syntactic and semantic data quality issues that propagate undetected to downstream consumers without real-time validation. Grab addressed this challenge by developing a standardized contract testing and observability framework within its Coban platform. Stakeholders define schema rules and field-level semantic validations, which can be recommended using large language models and anonymized sample data. A transformation engine converts these contracts into inverse SQL queries executed continuously by a FlinkSQL Test Runner on a dedicated consumer group. Problematic records are published to an alert topic, archived to AWS S3, and surfaced via Slack notifications and UI field-highlighting across more than 100 critical Kafka topics.
Yuanzhe LiuGrab ·
How we built a custom vision LLM to improve document processing at Grab
Document processing for identity verification across Southeast Asia presents challenges due to varied layouts and non-Latin scripts. Traditional OCR and off-the-shelf vision models struggle with accuracy, high latency, or lack of regional language training data. Grab addressed this by creating synthetic regional datasets, using an automated labeling pipeline named Documint, and evaluating open-source multimodal architectures. After initial LoRA fine-tuning failed on complex scripts like Thai and Vietnamese, full-parameter fine-tuning of Qwen2-VL 2B yielded substantial gains. To optimize deployment costs and latency, the team constructed a custom 1B parameter model pairing a Qwen2-VL vision encoder with a Qwen2.5 0.5B language decoder, achieving performance within 3 percentage points of the 2B model at significantly lower latency.
Jia ChenGrab ·
Powering Partner Gateway metrics with Apache Pinot
Grab needed to power real-time analytics dashboards for its Partner Gateway, tracking API status codes and latency across datasets reaching 6.8 billion rows over 30-day windows. Initial aggregation queries on datasets exceeding 150GB frequently timed out past 10 seconds, failing to meet the platform's 300-millisecond service level agreement. To support low-latency Online Analytical Processing queries, Grab routed metric streams through Apache Kafka and Apache Flink into Apache Pinot. Query execution was then accelerated by partitioning Kafka topics by metric name, adding rounded time interval columns, and implementing Star-tree indexes for multidimensional pre-aggregation.
Alvis ChewGrab ·
Data mesh at Grab part I: Building trust through certification
Rapid business growth across multiple verticals led Grab's centralized data engineering model to become an unscalable bottleneck, resulting in duplicate pipelines, ambiguous ownership, and broken downstream dependencies. To resolve these issues, the organization initiated a data mesh journey called Signals Marketplace that decentralizes data management and treats data as a product. A central data certification system establishes formal data contracts covering schemas, SLAs, freshness, and retention, while assigning clear Business Data Owners and Technical Data Owners. Breaches in contract guarantees automatically generate Data Production Incident tickets to enforce accountability and root-cause fixes. Consequently, 75% of internal queries now target certified assets, redundant tables saw a 400% year-over-year deprecation increase, and the total number of top-used datasets dropped by over 58%.
Chun Rong PhangGrab ·
The evolution of Grab's machine learning feature store
Grab redesigned its initial machine learning feature store, Amphawa, to address high-dimensional data, complex entity retrieval, and versioning challenges during feature updates. The new architecture adopts a feature-table model where data scientists output Parquet datasets to Amazon S3 using Spark, which are then atomically ingested into Amazon Aurora PostgreSQL via a reverse ETL workflow. To prevent noisy-neighbor contention and optimize infrastructure costs, the platform utilizes Aurora's distributed storage to separate reads from writes. Grab pairs Aurora Serverless on writer nodes to scale up during daily batch ingestion with Provisioned instances on read replicas for steady serving traffic.
Daniel TaiGrab ·
The complete stream processing journey on FlinkSQL
Grab previously relied on Apache Zeppelin notebooks for interactive stream processing exploration, but faced lagging Flink version upgrades, five-minute cluster cold starts, and poor integration with internal platforms. To address these limitations, the team migrated to a shared FlinkSQL gateway architecture structured into compute, integration, and query layers. The new setup uses a Hive Metastore catalog to expose Kafka topics as relational tables, while a custom control plane handles authentication and headless REST APIs over Flink's native interface. For production workflows, a configuration-based portal accepts SQL logic and automatically provisions and deploys Flink pipelines within ten minutes. This transition reduced ad-hoc query response times to under one minute and eliminated the need to maintain version adapter shims.
Calvin Tran