Alternatives to Mixpanel

This time I want to share something different. For those that have read my stack decisions, it's normal to expect some advice on infrastructure or React Native. Lately my mind has been focusing more on product as a experience than what's it made of (anatomy). As a tech leader, I have to worry about things like: are we taking enough time for reviews? Are we improving over time? Are we faster now? Is our code of higher quality?

For all these questions you can add many great recommendations on your pipeline. We use Trello for bug-tracking and project management. We use https://danger.systems/js/ to add checks for linting, type-enforcing and other quality dimensions in our PRs and a great feature from Vercel that let's you previsualize deployments directly in a PR. However it's not easy to measure this improvements over time. For customer matters we have Amplitude or Firebase analytics, but for our internal process? That's a little bit more complicated.

I collaborated recently with some folks in a small startup as an early adopter to create a metrics dashboard for engineers. I tried to add the tool to stackshare.io but still it doesn't appear as one of the options, please take a look on it over product hunt and let us know https://www.producthunt.com/posts/scope-6

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

We used to use Google Analytics to get audience insights while running a startup and we are constantly doing experiments to lear our users. We are a small team and we have a lack of time to keep up with trends. Here is the list of problems we are experiencing: - Analytics takes too much time - We have enough time to regularly monitor analytics - Google Analytics interface is too advanced and complicated - It's difficult to detect anomalies and trends in GA

We considered other solutions on a market, but found 2 main issues: - The solution created for analytic experts - The solution is pretty expensive and non-automated

After learning this fact we decided to create AI-powered Slack bot to analyze Google Analytics and share trends. The bot is currently working and highlights trends for us.

We are thinking about publishing this solution as a SaaS. If you are interested in automating Google Analytics analysis, drop a comment and you'll get an early access.

We will implement this solution only if we have 20+ early adaptors. Leave a message with your thought. I appreciate any feedback.

In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.

While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.

In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.

#ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService

At Heap, we searched for an existing tool that would allow us to express the full range of analyses we needed, index the event definitions that made up the analyses, and was a mature, natively distributed system.

After coming up empty on this search, we decided to compromise on the “maturity” requirement and build our own distributed system around Citus and sharded PostgreSQL. It was at this point that we also introduced Kafka as a queueing layer between the Node.js application servers and Postgres.

If we could go back in time, we probably would have started using Kafka on day one. One of the biggest benefits in adopting Kafka has been the peace of mind that it brings. In an analytics infrastructure, it’s often possible to make data ingestion idempotent.

In Heap’s case, that means that, if anything downstream from Kafka goes down, we won’t lose any data – it’s just going to take a bit longer to get to its destination. We also learned that you want the path between data hitting your servers and your initial persistence layer (in this case, Kafka) to be as short and simple as possible, since that is the surface area where a failure means you can lose customer data. We learned that it’s a very good fit for an analytics tool, since you can handle a huge number of incoming writes with relatively low latency. Kafka also gives you the ability to “replay” the data flow: it’s like a commit log for your whole infrastructure.

#MessageQueue #Databases #FrameworksFullStack

Segment has made it a no-brainer to integrate with third-party scripts and services, and has saved us from doing pointless redeploys just to change the It gives you the granularity to toggle services on different environments without having to make any code changes.

It's also a great platform for discovering SaaS products that you could add to your own – just by browsing their catalog, I've discovered tools we now currently use to augment our main product. Here are a few:

• Heap: We use Heap for our product analytics. Heap's philosophy is to gather events from multiple sources, and then organize and graph segments to form your own business insights. They have a few starter graphs like DAU and retention to help you get started.
• Hotjar: If a picture's worth a thousand words, than a video is worth 1000 * 30fps = 30k words per second. Hotjar gives us videos of user sessions so we can pinpoint problems that aren't necessarily JS exceptions – say, logical errors in a UX flow – that we'd otherwise miss.
• Bugsnag: Bugsnag has been a big help in catching run-time errors that our users encounter. Their Slack integration pings us when something goes wrong (which we can control if we want to notified on all bugs or just new bugs), and their source map uploader means that we don't have to debug minified code.

Hello, We are a medical technology company looking to integrate an in-app analytics tool. We've evaluated Mixpanel, Pendo, and Heap and are most impressed that Heap will solve our issues. We'd like to be able to determine not only clicks (con of Pendo) but also swipes and other user gestures within our app. Not sold on all three of these, can also look at other tools. We use Cordova, so hoping to find something compatible with that. Any advice?

Thanks

Can either of these (Pendo, and Amplitude) also function as a data warehouse for data we want to retain? How well can they accept data from other systems? I know they focused on session behavior. I would like to hear if anyone took their implementation further than session behavior?

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.