The video streaming company serves over 550 billion events per day, equaling roughly to 1.3 petabytes of data. The data infrastructure at Netflix is one of the most sophisticated in the world. Fig: Some of the data-related technologies used in 500px. All in all, this infrastructure supports around 60 people distributed across a couple of teams within the company, prior to their acquisition by Visual China Group. Periscope Data is responsible for building data insights and sharing them across different teams in the company. Splunk here does a great job of querying and summarizing text-based logs. Data from these DBs passes through a Luigi ETL, before moving to storage on S3 and Redshift. From a customer-facing side, the company’s web and mobile apps run on top of a few API servers, backed by several databases – mostly MySQL. The whole data architecture at 500px is mainly based on two tools: Redshift for data storage and Periscope for analytics, reporting, and visualization. Here one of our dashboards that shows you how you can track queries from Mode down to the single user: 3. That’s why we’ve built Integrate.io to provide Mode users with all the tools they need to optimize their queries running on Amazon Redshift. With ever-increasing calls to your data from analysts, your cloud warehouse becomes the bottleneck.
Mode makes it easy to explore, visualize, and share that data across your organization.īut as data volume grows, that’s when data warehouse performance goes down. The main data storage is obviously left to Redshift, with backups into AWS S3.įinally, since Redshift supports SQL, Mode is perfectly suited for running queries (while using Redshift’s powerful data processing abilities) and creating data insights. Segment is responsible for ingesting all kinds of data, combining it, and syncing it daily into a Redshift instance. Their efforts converged into a trio of providers: Segment, Redshift, and Mode. They tried out a few out-of-the-box analytics tools, each of which failed to satisfy the company’s demands.Īfter that, Clearbit took building the infrastructure in their own hands. ClearbitĬlearbit was a rapidly growing, early-stage startup when it started thinking of expanding its data infrastructure and analytics. In such a way, the data is easily spread across different teams, allowing them to make decisions based on data. Reports, analytics, and visualizations are powered using Periscope Data. The warehouse of choice is Redshift, selected because of its SQL interfaces and the ease with which it processes petabytes of data. Similar to many solutions nowadays, data is ingested from multiple sources into Kafka before passing it to compute and storage systems.
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The main problem then is how to ingest data from multiple sources, process it, store it in a central data warehouse, and present it to staff across the company. Instead of the analytics and engineering teams to jump from one problem to another, a unified data architecture spreading across all departments in the company allows building a unified way of doing analytics. It’s important for the entire company to have access to data internally. Getting data-driven is the main goal for Simple. And with that – please meet the 15 examples of data pipelines from the world’s most data-centric companies.
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