RudderStack — the creators of the leading open source Customer Data Platform (CDP) — needed a scalable way to collect and store metrics related to customer events and processing times (down to the nanosecond). They provide their clients with data pipelines that simplify data collection from applications, websites, and SaaS platforms. RudderStack's solution enables clients to stream customer data in real time — they quickly deploy flexible data pipelines that send the data to the customer's entire stack without engineering headaches. Customers are able to stream data from any tool using their 16+ SDK's, and they are able to transform the data in-transit using JavaScript or Python. How does RudderStack use a time series platform to provide their customers with real-time analytics? Join this webinar as Ryan McCrary dives into: RudderStack's approach to streamlining data pipelines with their 180+ out-of-the-box integrations Their data architecture including Kapacitor for alerting and Grafana for customized dashboards Why using InfluxDB was crucial for them for fast data collection and providing single-sources of truths for their customers

1. Streamline and Scale Out Data
Pipelines with Kubernetes,
Telegraf, and InfluxDB

2. What is RudderStack?

3. RudderStack architecture diagram
TRANSFORMATIONS
IDENTITY RESOLUTION
DATA GOVERNANCE

4. Rudderstack
Key Differences
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WAREHOUSE FIRST
DEVELOPER FOCUSED
NO PERSISTENT DATA /
FUTURE-PROOF YOUR DATA STACK
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5. Rudderstack
Key Differences
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WAREHOUSE FIRST
DEVELOPER FOCUSED
NO PERSISTENT DATA /
FUTURE-PROOF YOUR DATA STACK
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6. How do we use InfluxDB?

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Kubernetes Deployment
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Kubernetes Deployment
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What do we need?
● Fast
● Reliable
● Gets out of the way
Not our actual
business (but very
important to it)
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Why InfluxDB?
We constantly evaluate the best tools for our customers
In some deployments we run prometheus in parallel for comparison
We knew we needed a fast, efficient time series database
We have been using InfluxDB since we started storing metrics
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How do we use it?
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How do we use it?
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How do we use it?
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Metrics metrics everywhere
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Metrics metrics everywhere
● 100s of metrics
● High level
● Low level
● Counters, Timing, Guages
● Adding new metrics is fast
and easy
● Primary monitoring/alerting
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What’s next?
● Determining long term storage of metrics
○ Sample?
○ Compress?
● Migration of metrics as deployment model changes
● Any change must be supported forever (open source)
● InfluxDB is limited to 200 tags
● Always evaluating more efficient options
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17. Questions