Kshitij Grover’s Post

📑 How do you get Stripe data into a spreadsheet? If you’re manually exporting CSVs or copy-pasting row after row and re-formatting, guess what? You can save yourself hours of manual grief and start syncing automatically. This step-by-step guide will show you how to sync Stripe data to Google Sheets automatically and continually whenever changes occur in Stripe, so your spreadsheet will always contain the latest details, no more exports required. 💼 https://bit.ly/3SwW6k8 #Unito #Stripe #GoogleSheets #DataSync #RealTimeInsights

How does Stripe ensure end-to-end data reliability? Here, we walk through some real-life lessons learned and best practices for doing just that: https://hubs.la/Q01Z8Kx-0

How does Stripe ensure end-to-end data reliability? Here, we walk through some real-life lessons learned and best practices for doing just that. https://lnkd.in/gKvqb6du

When might a company like Stripe adopt Datahub? When growing pains put a strain on data governance, manually doing things begins to hinder data observability as a result. When Stripe became a prominent payroll platform, their product team needed better infrastructure to support data pipelines, monitor landing times, and pull performance metrics. Different teams were having to write complex data-based queries or manually click through hundreds of job status pages in order to gather insights into how jobs were performing. They built their own pipeline-specific dashboards, which was tedious, inconsistent among groups, and error-prone. And it made monthly reports problematic. Teams had 72 hours to generate a summary of 1,000 data jobs with complex dependencies, and no central source of truth. They resorted to frantic Slack messages, and manual inputs to assimilate some sort of digestible narrative. Even asking a question like, “How far along are we in the pipeline?” was challenging. This problem is far from unusual. We see it often among our clients at Acryl, and other DataHub adopters, like Stripe. As organizations grow and deal with an increasing volume of data, manual processes and disconnected systems that previously worked become unsustainable. Stripe needed a unified data catalog where all teams could get insights into their jobs, including run time, latency, status, and estimated delivery times. By integrating DataHub into the system, they were able to track lineage, quickly and accurately pull status updates, and unify all teams on one dashboard. Much better solution than pinging everyone on Slack.

Are you part of a business using Stripe and want more answers out of your Stripe data? I’ve got good news. Stripe is one of the easiest systems to extract data from. Just look at this catalog of connectors. Stripe leads the pack in # of connectors at 14. https://lnkd.in/gcs9KYSv Personally - of the 30+ source systems from which I’ve built data pipelines, Stripe is among the top 3 easiest to get data out of and work with. I would also put Salesforce in the top 3 - I love the ability to write SOQL. If you’re keen to use an AWS Redshift or Snowflake data warehouse, Stripe has their own ‘easy button’ to load your Stripe data into your data warehouse. They even include their own AI assistant to help you write queries. Note - the cost is 3 cents per transaction on Stripe. https://lnkd.in/g_Z52mSE If you want to avoid that cost or you’re not on AWS Redshift or Snowflake, you can use Fivetran to extract from Stripe to almost any database under the sun. Fivetran gives you up to 500k records/month free. You’ve got to be doing some serious volume on Stripe before you get close to paying for Fivetran. Stripe has a fantastic dashboard showing high level metrics e.g. MRR over time, churn. However, if you want to dig deeper - say analyze MRR over time by segment (which lives in your CRM), the Stripe dashboard can't help you with that. At that point, building a data pipeline out of Stripe might make sense. If you’ve got questions about how to get answers out of your Stripe data, please comment below or DM me. Happy to share my experience. Source systems from which I've built data pipelines: https://lnkd.in/gz5BZ6sD #stripe #data

📢 Are you a startup or small business using Stripe for payment processing? 🚀 I'm thrilled to share an exciting way of examining of Stripe data, that might take your data handling and reporting to another level. In this guide, you learn how to sync data from Stripe directly into AWS Dynamo DB and create a dynamic dashboard in ReTool to display your sales data and MRR (Monthly Recurring Revenue) in almost real-time. And more importantly without writing a single line of code. 💼 💳 🏦 What’s so different about this guide? Let's take a look: 1️⃣ Say goodbye to delayed MRR calculations! Stripe's native dashboard has a 24-48 hour delay, but with sync of data through Project Compound, you can get the MRR values in almost real-time, keeping you updated on your revenue streams like never before! 💸 2️⃣ Protect sensitive customer data! Grant your internal team members secure access to ReTool, guarding against potential data leaks and ensuring a safe working environment. 🔒 3️⃣ Swift data retrieval! With Project Compound, data is seamlessly written to AWS Dynamo DB, enabling lightning-fast queries in ReTool, without dealing with the paginated limitations of native Stripe API connections. 🚀 4️⃣ Efficiency at its best! ReTool's data caching capabilities optimize the querying process, using cached data when multiple team members query with same parameters 🔄 5️⃣ Keep control of your data! All the infrastructure deployed with Project Compound remains under your domain, ensuring your valuable data stays secure and doesn't bounce around third-party servers. 🛡️ Who should hop on this opportunity? 🎯 🛒 Businesses relying on Stripe for one-time fees or subscription-based payments will greatly benefit from this tutorial. 🌐💡 Check out the tutorial here: 🔗 https://lnkd.in/ghemBK4W #stripe #dynamodb #ReTool #ProjectCompound #businessintelligencetools #dataanalytics

Looking to ingest Stripe data into your data warehouse? Don't depend on a 3rd party application or Stripe's paid data pipeline product. Instead, use an open-source solution like Airbyte. This allows you to control your Stripe data directly from the source, not waiting on another application to ingest this data for you. Stripe tables can be synced using full refresh or incremental sync modes, depending on the table you are ingesting. A few things to keep in mind: - all sync modes may not be available for all tables - Stripe may not provide all historical data for you to sync (EX: the events table only looks back at the last 30 days of data) - you'll need to select a cursor field and primary key if using incremental - consider storage costs and run-times I've personally had great success with using open-source data ingestion tools. You can keep costs low (unlike with other more enterprise tools) and fully manage the data as you wish. #dataingestion #dataengineering

How #stripe Leverages #apachekafka and #apacheflink for Change Data Capture of over $640 Billion in payment volume" => Being in the #payments space, Stripe requires strict correctness and freshness guarantees. They rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. Stripe heavily relies on CDC to capture data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many critical financial reporting systems totaling over $640 Billion in payment volume annually. Stripe uses Flink’s flexible DataStream API components to perform aggregations and abstract the complexities of #streamprocessing from the downstream. In this slide deck, Stripe walks through the experience from the very beginning to what they have in production today. They share stories about the technical details and trade-offs we encountered along the way. https://lnkd.in/ePvXmtcZ

🚨 SaaS founders 🚨 One of our most requested templates just went live. It's built off of Stripe data. All of the queries are pre-written for you. So are the models. Out of the box you’ll get two of the most important ways to measure your business. 1️⃣ You get an ARR build: your ARR broken into its component parts. → New ARR → Expansion ARR → Contraction ARR → Churned ARR and churn rates 2️⃣ Cohorts. See how logo and retention evolve over time. My favorite way to measure any SaaS business. These are the models we use internally at Equals, and what I used at Intercom for the first several years. And you can ultimately customize these workbooks to give you exactly what you need. → If you need to filter the queries to remove certain accounts, we’ve indicated for you where to do that. → If you want to start forecasting your business from here, well you have the full power of a spreadsheet from which to do that. Curious to hear what you think! https://lnkd.in/gVSvTHbm

🧐 Double-click on your revenue data from Stripe with Equals. With a live connection to Stripe, you can put your ARR and NRR reporting on autopilot. And slice and dice it which ever way you want. Model your ARR Build today. 👉 https://lnkd.in/eA2VEjU6 Learn more on our blog 👇