Analytics
We track 27 posts about Analytics from 15 engineering blogs. Most active: Clickhouse, Helpshift, Cloudflare. Latest post: Oct 8, 2026.
Companies writing about Analytics
Recent posts
Why BigQuery can’t match ClickHouse Cloud for real-time analytics (opens on the source site)
ClickHouse Cloud delivered 438× better performance per dollar than BigQuery in CostBench. We trace the gap from fresh data arriving to fast answers coming back.
Why Databricks can’t match ClickHouse Cloud for real-time analytics (opens on the source site)
ClickHouse Cloud delivered 752× better performance per dollar than Databricks in CostBench. We trace the gap from fresh data arriving to fast answers coming back.
Smarter personalization: How property data helps us understand user price sensitivity (opens on the source site)
Grab ·
Introduction Existing systems estimate user price sensitivity primarily from spending behavior or demographic proxies. They do not systematically account for residential property values, which can indicate a user’s financial circumstances. This omission creates three limitations: Limited property insights: Existing profiles do not account for property values. One-dimensional profiles: Users with similar spending patterns but different living standards receive the same classification. Regional variation: Manual classification does not adapt well to differences between property markets. This…
How LinkedIn extended ClickHouse from distributed tracing to metric discovery and analytics (opens on the source site)
LinkedIn expanded its ClickHouse observability stack from distributed tracing to metric discovery and analytics, consolidating 13B+ metrics into one index serving 150k+ queries per minute.
Identify AI model overuse with User Insights (opens on the source site)
AI Gateway User Insights now adds task, model, turn, and user categories to help teams understand AI adoption and make better model decisions. This is available free to AI Gateway users.
Is your domain using post-quantum encryption? Now you can see for yourself (opens on the source site)
Cloudflare has added visibility into post-quantum (PQ) encryption in TLS 1.3 directly into HTTP Analytics, Log Explorer, and Logpush. Learn how to make sure your domain is protected with PQ encryption.
How fast is the web? Explore billions of real-user measurements with BEACON (opens on the source site)
Cloudflare is open-sourcing the BEACON dataset, making billions of anonymized Real User Monitoring (RUM) performance records publicly available on Google BigQuery. Explore real-world Core Web Vitals, soft navigation metrics, and performance breakdowns across browsers and regions.
Postgres on NVMe: performance and the convergence of transactions and analytics (opens on the source site)
See how local NVMe transforms Postgres transaction performance—and why ClickHouse remains essential for fast analytics as workloads scale.
The Agentic Analytics Benchmark: Measuring model accuracy and efficiency in analytical agents (opens on the source site)
We took 201 real analytics questions from our production data warehouse, benchmarked 28 models on correctness, cost, and speed, and released an open harness so you can run the same test on your own.
How to Install Matomo on Ubuntu 26.04 (opens on the source site)
Matomo, formerly known as Piwik, is open-source web analytics software used to track websites’ visitors. As an alternative to Google ... Read More The post How to Install Matomo on Ubuntu 26.04 appeared first on RoseHosting.
AWS Glue 6.0 now available with 30% lower price and full Apache Iceberg v3 support (opens on the source site)
AWS ·
AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.13, and Scala 2.13, delivering 30% lower pricing than previous AWS Glue versions.
How Sony LIV uses ClickHouse Cloud to deliver live streaming analytics at billion-row scale (opens on the source site)
Sony LIV consolidated fragmented batch, Elasticsearch, and BigQuery workloads on ClickHouse Cloud, delivering sub-second analytics across billions of daily streaming events.
How AI is transforming analytics at Grab (opens on the source site)
Grab ·
Introduction At Grab, analytics sits close to almost every decision that matters. Our north star is the democratisation of intelligence, ensuring that anyone making a business call has immediate access to trustworthy answers. Over the last two years, model capability has crossed a threshold enabling this shift. Agents now do in minutes what used to take a week: preparing the data, writing queries, running deep analysis and developing insights for business opportunities, designing experiments and interpreting the results, drafting the commentary that follows, and more. Our throughput is no…
Modeling Device Capabilities for Analytics (opens on the source site)
Netflix ·
by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh SelverajNetflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live streaming and cloud gaming, across a diverse ecosystem of devices. However, not all devices are created equal. Hardware limitations such as available RAM, CPU cores, display capabilities, or platform support mean that some features cannot be supported on certain device models. To ensure the best possible user experience, we rely on a deep understanding of device capabilities. We have invested in…
From Day 1 to Production: Building Lyft’s Analytics & Rides Intelligence Assistant as Onboarding… (opens on the source site)
Lyft ·
From Day 1 to Production: Building Lyft’s Analytics & Rides Intelligence Assistant as Onboarding ProjectWritten by Sagar Baronia at Lyft.A Different Kind of Day OneMost onboarding journeys follow a familiar arc: orientation sessions, benefits enrollment, setting up your laptop, and gradually finding your footing over the first few weeks. Mine followed that arc too, but with an additional thread running alongside it from the very start.I joined Lyft in March 2026 as a Senior Data Scientist — Algorithm on the Marketing, Business & Ads team, bringing close to a decade of experience in data…
Bootstrap Confidence Intervals for LLM Evaluation (opens on the source site)
Indeed ·
Introduction As Large Language Models (LLMs) move from research prototypes to production systems, the developers of these systems need rigorous performance evaluation. In particular, we need confidence intervals around estimates of system accuracy. However, LLMs introduce a challenge that is unusual for ML systems: they are (operationally) non-deterministic. Even with the temperature set to zero, […]
Building a Modern Analytics Stack Around ClickHouse (opens on the source site)
Historically, relational databases did double duty. The same PostgreSQL / MySQL instance that handled your application’s writes also answered your business questions. A well-indexed schema, a few GROUP BY reports, done. And that works, right up until it doesn’t: the reports get slower, the dashboards start eating the same I/O budget as the application; or, […] The post Building a Modern Analytics Stack Around ClickHouse appeared first on Severalnines.
From Chaos to Clarity: How We Built a Unified, Self-Routing Support Ops Ticketing System at Lyft (opens on the source site)
Lyft ·
Written by Atul Gupta, Analytics Manager — LUS Support Ops, LyftAt Lyft, getting operators and riders connected quickly and reliably depends on more than technology — it depends on the teams working behind the scenes to keep that technology running smoothly. For the operators managing Lyft’s fleet across markets, having fast, reliable access to support is what keeps bikes on the road, stations stocked, and issues resolved before they affect riders. Building the infrastructure that makes that support possible is what our team does; this is the story of how we built it.When I first joined Lyft…
Advanced Partitioning Strategies for PostgreSQL OLTP and Analytics Datasets at Scale (opens on the source site)
When PostgreSQL tables are still relatively small, most tasks seem straightforward. You can run queries without thinking too much about indexes, retention jobs are manageable, and even vacuum operations usually stay under control. But things change pretty quickly once tables start growing into hundreds of millions or billions of rows. At that scale, even simple […] The post Advanced Partitioning Strategies for PostgreSQL OLTP and Analytics Datasets at Scale appeared first on Severalnines.
Why we moved our growth analytics back into Tinybird (opens on the source site)
Tinybird ·
The story of Birdwatcher, Tinybird's internal growth analytics stack, and why we moved more of our analytics loop onto our own data.
Smarter Ad Spending: 5 Marketing Analytics Strategies to Optimize Your Budget (opens on the source site)
Toptal ·
Learn expert-backed strategies to optimize your advertising budget, avoid wasted spend, and drive higher marketing ROI through data-informed decision-making.
Revamping Myntra App Analytics persistence with KMP and SQLite (opens on the source site)
Myntra ·
IntroductionIn the dynamic landscape of e-commerce, data is the bedrock of decision-making, and app stability is the foundation of user experience. At Myntra, where millions of users engage with our platform daily, ensuring the reliability of both is paramount. This necessity drove us to re-architect a critical piece of our infrastructure: the persistence layer of the Myntra app’s analytics SDK. This post details our journey of augmenting our legacy analytics SDK with a robust new persistence engine backed by SQLite[1], achieving significant gains in app stability and data…
Operating Trino at Scale With Trino Gateway (opens on the source site)
Expedia ·
Expedia Group Technology — DataWorkload‑aware routing for TrinoPhoto by Joseph Barrientos on UnsplashTrino — a fork of PrestoSQL — is a powerful tool in modern data analytics, enabling organizations to query large datasets quickly and efficiently. As a distributed SQL query engine, Trino provides fast, scalable insights without requiring data relocation. While Trino is robust on its own, its capabilities are further enhanced when paired with a Gateway, which introduces features such as query routing, strong security, and streamlined cluster management.A brief overviewThe Gateway project…
From Data to Insight: Helpshift’s Journey with ML Observability (opens on the source site)
IntroductionIn an age where artificial intelligence (AI) and machine learning (ML) are integral to almost every aspect of our lives, ensuring the effectiveness, fairness, and reliability of ML models is paramount. Observability plays a crucial role in maintaining the performance of these models, allowing us to detect and resolve issues promptly. At Helpshift, we recognized the need for robust ML observability to keep our models running smoothly and efficiently.This blog post explores our journey in building a custom ML observability solution tailored to our specific needs. We’ll delve into…
The Hidden Layer of Analytics: How QA Builds Trust in Data (opens on the source site)
Every accurate metric is backed by countless validations, events checks and integrity tests in the background.IntroductionQuality Assurance in the data-driven systems extends beyond UI validation and backend verification. Such systems rely heavily on data precision and accuracy.A recent QA focused on validating a productivity analytics framework, ensuring that every event, metric and data flow accurately represented real-world user behaviour. The process was primarily manual, involving live simulations, event validation and detailed metric verification across environment which emphasised…
How Data Powers Agent Productivity (opens on the source site)
As a data engineer, I used to see metrics as just numbers on a dashboard — until I realized they’re the lens through which customers view and run their operations. In customer support, for example, agent productivity metrics aren’t just figures, they’re actionable insights that drive efficiency, shape staffing decisions, and directly impact customer satisfaction.These aren’t just charts — they help customers understand the value we provide, how well things are working, and what decisions to make next. Realizing this changed how I think about building analytics.➡️💡The Question That Shifted…
The Google Analytics Setup I Use on Every Site I Build (opens on the source site)
Google Analytics is a powerful yet quite complicated tool. And unfortunately, the truth is most people who use it don’t reap its full benefits. There’s a lot of excellent and free content out there that explains how to use Google Analytics, but most of it is rather narrowly focused on use cases that primarily apply to marketers and advertisers; very little is geared toward web developers who simply want to better understand how people are using the sites they build. For the past three years I’ve worked on Google Analytics (specifically on the web tracking side), and in that time I’ve learned…
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