# Analytics Pipeline Published articles for Analytics Pipeline. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Procore is powering construction analytics with OpenSharing DevFeed: [How Procore is powering construction analytics with OpenSharing]() Original publisher: [Read original article]() Author: Krishan Kumar Published: 2026-10-29T00:29:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Analytics Pipeline](), [data-engineering](), [Data Quality](), [semantic-layer](), [schema-evolution](), [protocols](), [DataOps]() Tags: [2](), [ai](), [ai-use](), [all](), [also](), [analysis](), [analytics](), [analytics-pipeline](), [api](), [apis](), [app](), [application](), [architecture](), [area](), [asia](), [australia](), [company](), [construction](), [customers](), [data](), [one](), [with]() ### AI overview Procore Analytics 2.0 replaces a SQL Server pipeline fed by public API extractions--which allowed at most three refreshes per day--with Databricks OpenSharing. Procore cleans, validates, and models data in a multi-region medallion pipeline, then shares gold tables to customer environments. Customers without Databricks connect with an OpenSharing token and client libraries; Databricks customers can receive shares natively. Unity Catalog permissions are reconciled automatically per user and project, and Change Data Feed lets recipients sync only updates or the full dataset. Procore reports expanding its extract catalog to hundreds of tables in less than half the time taken to build the first version, while customers can schedule refreshes to suit their tools. It also sets release notice periods: six weeks for four annual breaking changes and three weeks for twelve non-breaking releases. The source describes the architecture and reported benefits but provides no measured refresh latency or quantified cost comparison. ### Source excerpt Connecting an industry built on disconnected dataConstruction runs on data that rarely sits in one place... ## Build a Reliable Analytics Pipeline Before Migrating to a Data Warehouse DevFeed: [Build a Reliable Analytics Pipeline Before Migrating to a Data Warehouse]() Original publisher: [Read original article]() Author: tbutt4438 Published: 2026-10-28T04:28:05Z Content type: tutorial Language: en Sources: [SitePoint]() Topics: [Analytics Pipeline](), [etl](), [metric-standardization](), [DataOps]() Tags: [access-controls](), [agile-development](), [ai](), [airflow](), [analytics](), [analytics-pipeline](), [apache-airflow](), [architecture](), [batch](), [concurrency](), [dashboard](), [data](), [data-quality](), [data-warehouse](), [database](), [dataset](), [dbt](), [pipeline](), [warehouse]() ### AI overview Build and test a small analytics pipeline before choosing a warehouse: define each metric's date, currency, status and reporting grain, then preserve raw deliveries, resolve versions in staging, and aggregate in reporting models. In the PostgreSQL-compatible example, rank all versions before filtering status so a later refund supersedes an earlier paid state; deduplicate retries and flag conflicting payloads for the same source version. Add failing-row checks for data assumptions, and test late older updates and repeated deliveries. Incremental runs should recompute changed IDs and affected old and new reporting groups, handle deletions by an explicit policy, and advance checkpoints only after outputs commit. Track ingestion, transformation and validation freshness separately; a last validated result with a warning may be safer than serving an unverified total. Measure performance, concurrency, freshness needs and application impact before migration. The excerpt notes that dbt can organize tests and Airflow guidance addresses retryable tasks; neither tool alone ensures source completeness or correct metric definitions. ### Source excerpt null Continue reading Build a Reliable Analytics Pipeline Before Migrating to a Data Warehouse on SitePoint. ## Build a Reliable Analytics Pipeline Before Moving to a Data Warehouse DevFeed: [Build a Reliable Analytics Pipeline Before Moving to a Data Warehouse]() Original publisher: [Read original article]() Author: tbutt4438 Published: 2026-10-21T13:16:13Z Content type: tutorial Language: en Sources: [SitePoint]() Topics: [Analytics Pipeline](), [etl](), [Amazon Redshift](), [metric-standardization](), [DataOps](), [Apache Spark]() Tags: [agile-development](), [ai](), [analytics](), [analytics-pipeline](), [data-quality](), [data-warehouse](), [pipeline]() ### AI overview Define a metric contract and reporting grain before choosing a warehouse. In the example, raw order deliveries are preserved, duplicates and versions are resolved in staging, and reporting includes only each order's latest state. Ranking versions before filtering status prevents a refunded order's older paid version from inflating sales: the example's correct total is 12,500 cents, not 27,500. Add failing-row and behavior tests for conflicting versions, late arrivals, and retries. Incremental pipelines must recompute changed orders and affected old and new reporting groups; deletions need explicit handling. PostgreSQL can prototype the pipeline, dbt can organize maintained SQL models and tests, and Airflow can coordinate dependent jobs and retries. These tools address different responsibilities and do not ensure source completeness. Measure performance, concurrency, freshness, and application impact before migrating, and expose freshness and validation status with dashboard results. ### Source excerpt null Continue reading Build a Reliable Analytics Pipeline Before Moving to a Data Warehouse on SitePoint. ## How to Build a PostgreSQL-to-LLM Analytics Pipeline with Node.js DevFeed: [How to Build a PostgreSQL-to-LLM Analytics Pipeline with Node.js]() Original publisher: [Read original article]() Author: Jhon-Harry Published: 2026-09-24T13:44:09Z Content type: tutorial Language: en Sources: [SitePoint]() Topics: [PostgreSQL](), [Large Language Model](), [Analytics Pipeline](), [etl](), [Node.js](), [Database](), [Security]() Tags: [analytics-pipeline](), [etl](), [how-to](), [llm](), [node-js](), [postgresql](), [statistics-and-analysis]() ### AI overview This tutorial shows how to build a Node.js pipeline that extracts a bounded, curated dataset from PostgreSQL, transforms it through an ETL layer, and sends it to a large language model for analytics. It emphasizes removing sensitive fields, controlling prompt size, using parameterized queries, and validating model output. ### Source excerpt null Continue reading How to Build a PostgreSQL-to-LLM Analytics Pipeline with Node.js on SitePoint. ## Gain visibility into client-side network failures with NEL DevFeed: [Gain visibility into client-side network failures with NEL]() Original publisher: [Read original article]() Author: Achraf Souk Published: 2026-08-21T19:06:03Z Content type: tutorial Language: en Sources: [Networking & Content Delivery]() Topics: [observability](), [Security Information and Event Management (SIEM)](), [telemetry](), [Amazon API Gateway](), [Amazon S3](), [AWS Lambda](), [Analytics Pipeline](), [Serverless](), [cloud-infrastructure](), [HTTP](), [JSON](), [TLS (Transport Layer Security)](), [W3C]() Tags: [amazon-simple-storage-service-s3](), [analytics-pipeline](), [api](), [aws-lambda](), [connectivity](), [deploy](), [development](), [infrastructure](), [logs](), [network](), [networking-content-delivery](), [observability](), [technical-how-to](), [trace]() ### AI overview This tutorial explains how Network Error Logging (NEL) exposes client-side network failures that may not appear in server logs. It describes NEL and presents a serverless AWS pipeline using API Gateway, Data Firehose, Lambda, S3, Athena, and AWS CDK to collect, analyze, and observe NEL reports. ### Source excerpt When your user experiences a connectivity issue visiting your website, it's unlikely that you'll see a trace of it in your server logs. DNS resolution failures happen before your user ever connects to your server. TCP timeouts, on shared infrastructure such as Content Delivery Networks (CDNs), are difficult to attribute to a specific origin. Even [...] ## From search to checkout in 20 lines of code: building a 4-stage conversion funnel with OpenTelemetry DevFeed: [From search to checkout in 20 lines of code: building a 4-stage conversion funnel with OpenTelemetry]() Original publisher: [Read original article]() Author: Matthew Adams Published: 2026-08-06T00:00:00Z Content type: tutorial Language: en Sources: [Elasticsearch Labs]() Topics: [OpenTelemetry](), [elasticsearch](), [product analytics](), [Analytics Pipeline]() Tags: [analytics](), [analytics-pipeline](), [es-ql](), [opentelemetry](), [product](), [relevance](), [search-analytics]() ### AI overview This tutorial shows how to add OpenTelemetry spans for add-to-cart and purchase events, linking them to search queries with query IDs. It uses Elasticsearch Query Language (ES|QL) to analyze a four-stage funnel and attribute revenue to searches, while explaining attribution limits and how teams can use the results to prioritize search relevance work. ### Source excerpt Add cart and purchase tracking to your search analytics pipeline and use ES|QL to answer the question every product manager asks: which search queries drive the most revenue? ## Game integrity and cheat detection using AWS Game Analytics Pipeline, Amazon Quick, and Kiro agentic IDE DevFeed: [Game integrity and cheat detection using AWS Game Analytics Pipeline, Amazon Quick, and Kiro agentic IDE]() Original publisher: [Read original article]() Author: Gage McGilvra Published: 2026-07-23T14:17:50Z Content type: tutorial Language: en Sources: [AWS for Games Blog]() Topics: [Analytics Pipeline](), [Kiro](), [dashboards](), [data-processing](), [SQL](), [Python](), [Amazon S3]() Tags: [amazon-athena](), [amazon-kinesis](), [amazon-quick-sight](), [amazon-s3](), [amazon-simple-storage-service-s3](), [analysis](), [analytics-pipeline](), [aws-management-console](), [data-analysis](), [games](), [github](), [how-to](), [industries](), [kiro]() ### AI overview This tutorial shows how to extend AWS Game Analytics Pipeline with cheat-detection capabilities. It covers collecting integrity metrics, transforming game-event data with Athena SQL views, importing datasets into Amazon Quick Sight, and building ML-powered dashboards to identify anomalous player behavior. ### Source excerpt Cheating in online games erodes player trust, damages competitive integrity, and can drive players away. Game studios need a scalable analytics pipeline to detect anomalous player behavior. In this post, we show how to build a game integrity proof of concept (POC) using the Guidance for Game Analytics Pipeline on AWS, Amazon Athena, and Amazon [...] ## Good Data For Bad Golf DevFeed: [Good Data For Bad Golf]() Original publisher: [Read original article]() Author: jgamblin Published: 2026-05-13T14:05:06Z Content type: article Language: en Sources: [Jerry Gamblin]() Topics: [Python](), [CSV](), [GitHub Pages](), [GitHub Actions](), [Self-hosted]() Tags: [analytics-pipeline](), [csv](), [github-actions](), [github-pages](), [pipeline](), [python](), [self-hosted](), [uncategorized]() ### AI overview The article presents jgamblin/golf, a self-hosted Python analytics pipeline that converts Garmin Golf app CSV exports into a multi-page GitHub Pages dashboard. It describes features including session analysis, club-level consistency, session replay, gapping, coaching recommendations, and data-quality tracking, with deployment automated through GitHub Actions. ### Source excerpt I play golf. I am not good at golf. But I have a Garmin Approach R10 launch monitor, a Python interpreter, and too much free time, so naturally I spent way more time building a dashboard to analyze my swing data than I did actually swinging a club. The result is jgamblin/golf, a self-hosted analytics ... Read more ## Revamping Myntra App Analytics persistence with KMP and SQLite DevFeed: [Revamping Myntra App Analytics persistence with KMP and SQLite]() Original publisher: [Read original article]() Author: Kartik Sharma Published: 2026-03-31T13:01:09Z Content type: article Language: en Sources: [Myntra]() Topics: [SQLite](), [Persistence](), [Android](), [mobile-app-performance](), [Database](), [legacy systems](), [product analytics](), [Analytics Pipeline]() Tags: [analytics](), [analytics-pipeline](), [android](), [app](), [app-performance](), [app-stability](), [background](), [batch](), [bottlenecks](), [data](), [data-integrity](), [databases](), [e-commerce](), [engine](), [error-handling](), [event](), [events](), [ios](), [kmp](), [kotlin-multiplatform](), [mobile-app-development](), [persistence](), [sqlite]() ### AI overview Myntra replaced its analytics SDK's fragile persistence with a Kotlin Multiplatform pipeline backed by SQLite, improving event durability and Android app stability. The legacy Android queue rewrote a growing SharedPreferences file for each update, contributing to data loss, OOM errors, and ANRs; iOS LevelDB lacked transactional batch handling and robust background syncing. The new pipeline stores each event as a SQLite row, batches by priority, and uses coroutine serialization and mutexes. Android WorkManager and iOS BGProcessingTask schedule background flushes; shared logic sends fresh batches first, retries older events individually, and eventually purges repeatedly failing events. A configurable row cap prunes old unsynced events, while critically low storage triggers direct HTTP upload. Android rollout advanced from a canary through 1%, 5%, 10%, 50%, and 100%, with a discrepancy caught at 5%. Myntra reports more high-traffic events recovered and about 1.1 million fewer monthly ANR instances and 250,000 fewer monthly crashes attributed to the old pipeline. iOS remains in early A/B rollout. ### Source excerpt Introduction In 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 integrity. Background: The Limits of the Legacy Pipeline For years, our legacy in-house analytics SDK was the workhorse for collecting user interaction data. However, as our app's event volume grew, the SDK's aging persistence and processing logic created significant bottlenecks that led to degraded app performance and compromised the reliability of analytics data our product teams depended on. On Android: The Legacy Pipeline and its Limits On Android, the legacy analytics pipeline followed a straightforward event processing model: Capture, Persist & Batch: Events generated by used actions were held in an in-memory queue and then serialized into a persistent queue backed by SharedPreferences[2]. Once a batch threshold was met (typically 10 events), the batch was dispatched to the server. Background Flushing: Periodic background jobs flushed unsent events and retried failed batches. Error Handling: Failed batches were pushed to a separate retry queue, with older events silently purged if the queue hit its cap. High level overview of legacy android pipeline At the time of the initial implementation, SharedPreferences was a pragmatic choice to prioritize velocity. The ecosystem for local databases on Android was still maturing, and raw SQLite often required significant boilerplate compared to the simplicity of a key-value store. However, SharedPreferences is architecturally limited for high-throughp ## Finding the UFC GOAT: A Full Stack Pipeline with dlt, dbt and Metabase DevFeed: [Finding the UFC GOAT: A Full Stack Pipeline with dlt, dbt and Metabase]() Original publisher: [Read original article]() Author: Reshef Sharvit Published: 2026-02-04T00:00:00Z Content type: tutorial Language: en Sources: [DLT Hub]() Topics: [Analytics Pipeline](), [Test-driven development](), [Data visualization](), [streaming data analytics]() Tags: [analytics](), [analytics-pipeline](), [charts](), [community](), [database](), [dbt](), [pipeline](), [sql]() ### AI overview A software engineer describes building a UFC analytics project with dlt for data ingestion, PostgreSQL for storage, dbt for SQL transformations and tests, and Metabase for exploring visualizations. The pipeline brings fight statistics and context into queryable, analysis-ready tables; the author uses it to compare fighters across multiple metrics while noting that statistics do not capture every aspect of performance. ### Source excerpt Who's the UFC GOAT? Reshef Sharvit built an analytics pipeline using dlt, dbt & Metabase to find out. Automated ingestion meets deep SQL analysis. See the results here. ## How Dub Uses Webhooks to Power Features DevFeed: [How Dub Uses Webhooks to Power Features]() Original publisher: [Read original article]() Author: Jp Valery Published: 2025-11-19T00:00:00Z Content type: article Language: en Sources: [Resend]() Topics: [Publish-subscribe pattern](), [Iterable]() Tags: [analytics-pipeline](), [api](), [backend](), [database](), [developer](), [growth](), [guides](), [in-app-messaging](), [webhooks]() ### AI overview Dub uses Resend webhooks, batch sending, and message tags to add real-time read-status updates to its in-app messaging between brands and affiliates. The article describes how the email platform supports Dub's product features and handles email infrastructure as the service scales. ### Source excerpt Dub transforms Webhooks from simple notifications into powerful product features that drive in-app messaging and growth, sending 5 million emails each month. ## Why Flock Safety turned to ClickHouse for real-time vehicle traffic analytics DevFeed: [Why Flock Safety turned to ClickHouse for real-time vehicle traffic analytics]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-04-29T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [data-engineering](), [Amazon Redshift](), [software-architecture](), [data]() Tags: [amazon-redshift](), [analytics](), [analytics-pipeline](), [clickhouse](), [dashboards](), [data-architecture](), [data-engineering](), [real-time]() ### AI overview This developer case study explains why Flock Safety moved its vehicle traffic analytics platform from an Amazon Redshift, DBT, Prefect, and QuickSight SPICE pipeline to ClickHouse Cloud. The former architecture refreshed data daily, required up to four hours to process some datasets, and made data unavailable during refreshes. ClickHouse enabled real-time analytics for camera traffic without the previous size constraints or row-level security limitations. ### Source excerpt "With ClickHouse, our customers now have real-time analytics for their camera traffic, and there are no more constraints on size or row-level security." ~ Leon Kozlowski, Data Engineering Manager ## Real-time payment analytics: Building a data pipeline from Stripe to AWS DevFeed: [Real-time payment analytics: Building a data pipeline from Stripe to AWS]() Original publisher: [Read original article]() Author: James Beswick Published: 2025-03-14T00:00:00Z Content type: tutorial Language: en Sources: [Stripe Dot Dev Blog]() Topics: [Analytics Pipeline](), [Event-Streaming](), [stream financial data to Snowflake](), [streaming data to S3 with Parquet](), [stripe](), [Payment Fraud]() Tags: [analytics](), [analytics-pipeline](), [aws](), [data](), [data-pipeline](), [payment](), [pipeline](), [real-time](), [stripe](), [transactions]() ### AI overview A guide to building a scalable real-time payment analytics pipeline from Stripe to AWS, covering architectural components and implementation details for monitoring transactions, improving security, and analyzing customer behavior. ### Source excerpt Discover how to build a scalable real-time payment analytics pipeline from Stripe to AWS. This guide explores the challenges, architectural components, and implementation details to help businesses monitor transactions, enhance security, and gain insights into customer behavior. ## Real-time Personalized Shopping Experience With Estuary and Tinybird DevFeed: [Real-time Personalized Shopping Experience With Estuary and Tinybird]() Original publisher: [Read original article]() Author: Shruti Mantri Published: 2025-01-31T16:40:59Z Content type: tutorial Language: en Sources: [Estuary]() Topics: [streaming data analytics](), [Event-Streaming](), [Redpanda](), [CI/CD]() Tags: [analytics-pipeline](), [api](), [backend](), [cli](), [data](), [data-integration](), [e-commerce](), [estuary](), [inventory-management](), [mongodb](), [real-time](), [tinybird]() ### AI overview This tutorial explains how to build a real-time e-commerce clickstream pipeline with Estuary and Tinybird. It walks through capturing MongoDB change events, streaming them into Tinybird, and using SQL queries to support pricing, recommendations, inventory monitoring, and cart recovery. ### Source excerpt Boost e-commerce sales with real-time data integration. Optimize pricing, personalize shopping, and improve inventory management using Estuary and Tinybird. ## Logging millions of requests reliably with our new data ingestion pipeline DevFeed: [Logging millions of requests reliably with our new data ingestion pipeline]() Original publisher: [Read original article]() Published: 2022-07-01T00:00:00Z Content type: article Language: en Sources: [HackerEarth]() Topics: [Analytics Pipeline](), [data-engineering](), [streaming data analytics](), [Web app](), [Amazon S3](), [software-architecture](), [API Logging]() Tags: [amazon-kinesis](), [analytics-pipeline](), [architecture](), [compress](), [cost](), [data](), [data-ingestion](), [data-ingestion-pipeline](), [database](), [flow](), [http](), [https](), [kinesis](), [logging](), [managed](), [minutes](), [operational](), [overhead](), [pipeline](), [query]() ### AI overview HackerEarth replaced a complex, self-hosted request logging system with Amazon Kinesis Firehose delivery to Redshift, using retries to protect against intermittent component failures and reporting data availability within 10-15 minutes. As request volume grew, Redshift costs led the team to retain only 12 months there, reducing analyst access to historical logs. The article describes querying compressed hourly log files in S3 with serverless Athena instead. Athena's partition projection uses the S3 date-hour path, and queries must filter on the projected datehour column to limit scanned data and improve speed and cost. Athena workgroups can cancel queries that exceed scan limits; Athena also retains query results in S3 for 45 days, allowing eligible reruns to return stored results. The account presents these as ways to manage storage and query costs while accessing longer-term logs, but gives no quantified savings. ### Source excerpt Introduction HackerEarth handles millions of requests every day. To understand the user access patterns, get the usage of any particular feature or a page, figure out the daily active users or users who have been active for the past 6 months, etc in near real time, it is important to stream that data from across different services and ingest it to the analytics pipeline reliably. Problem Our old request logging architecture was complex and has many moving components. There were a lot of operational overheads involved in maintaining and scaling each of those components independently to ensure that all the self-hosted components were up and running all the time. Architecture Solution Last year, we revamped the way we log our web requests. It was done mainly to increase the reliability in logging the HTTP/HTTPS request data from across web services and also to reduce the operational overheads and the infrastructure cost associated with it. The new flow is making use of Kinesis Firehose data streams to deliver the request data from our web servers to Redshift (the database that we use to log and query request data) reliably with much lower cost. Amazon Kinesis Firehose is a fully managed service that automatically scales to match the throughput of our incoming request log data and requires no ongoing administration. It also allows us to compress and encrypt the data before loading it, minimizing the amount of storage used at the destination with increased security and we only need to pay for the amount of data we transmit through the service. The new flow is a fully managed solution with almost no operational overhead. We tried to keep the flow simple and straightforward with less number of moving components. The request data is now guaranteed to appear in the Redshift table within 10-15 minutes from the point we received a request at our web server's end. There are retries configured between consecutive infra components in the new flow to make sure there are no message ## Continuous Deployment for AWS Glue DevFeed: [Continuous Deployment for AWS Glue]() Original publisher: [Read original article]() Author: Mohammed Published: 2020-10-15T15:51:25Z Content type: tutorial Language: en Sources: [Brave New Geek]() Topics: [AWS Glue](), [Continuous Deployment (CD)](), [GitHub Actions](), [Jupyter Notebook](), [Amazon S3]() Tags: [analytics](), [analytics-pipeline](), [aws](), [aws-glue](), [ci-cd](), [continuous-delivery](), [continuous-deployment](), [etl](), [github](), [github-actions](), [jupyter](), [jupyter-notebook](), [s3](), [serverless]() ### AI overview A tutorial for automating continuous deployment of AWS Glue ETL jobs. It uses GitHub Actions to generate a Python script from a Jupyter notebook, copy it to Amazon S3, and update the Glue job to use the new script. ### Source excerpt AWS Glue is a managed service for building ETL (Extract-Transform-Load) jobs. It's a useful tool for implementing analytics pipelines in AWS without having to manage server infrastructure. Jobs are implemented using Apache Spark and, with the help of Development Endpoints, can be built using Jupyter notebooks. This makes it reasonably easy to write ETL processes in an interactive, iterative fashion. Once finished, the Jupyter notebook is converted into a Python script, uploaded to S3, and then run as a Glue job. ## Implementing ETL on GCP DevFeed: [Implementing ETL on GCP]() Original publisher: [Read original article]() Author: Deepmala Published: 2020-07-15T20:53:17Z Content type: tutorial Language: en Sources: [Brave New Geek]() Topics: [DataOps](), [Cloud](), [BigQuery](), [data loss prevention](), [Low code](), [No-code](), [olap]() Tags: [analytics](), [analytics-pipeline](), [bi-tools](), [bigquery](), [cdap](), [cloud-data-loss-prevention](), [cloud-dataflow](), [cloud-dataprep](), [cloud-dataproc](), [cloud-pub-sub](), [cloud-storage](), [cloud-tasks](), [data-analytics](), [data-fusion](), [data-lake](), [data-loss-prevention](), [elt](), [etl](), [gcp](), [no-code](), [serverless](), [sql]() ### AI overview A practical guide to building ETL pipelines on Google Cloud Platform using Google-managed services. It explains a two-phase architecture with Cloud Storage as a data lake, Cloud Data Loss Prevention for sensitive-data detection or redaction, and BigQuery as the curated data warehouse, with attention to low-code and no-code approaches. ### Source excerpt ETL (Extract-Transform-Load) processes are an essential component of any data analytics program. This typically involves loading data from disparate sources, transforming or enriching it, and storing the curated data in a data warehouse for consumption by different users or systems. An example of this would be taking customer data from operational databases, joining it with data from Salesforce and Google Analytics, and writing it to an OLAP database or BI engine.