Alternatives to Azure Application Insights

I've used more and more of New Relic Insights here in my work at Kong. New Relic Insights is a "time series event database as a service" with a super-easy API for inserting custom events, and a flexible query language for building visualization widgets and dashboards.

I'm a big fan of New Relic Insights when I have data I know I need to analyze, but perhaps I'm not exactly sure how I want to analyze it in the future. For example, at Kong we recently wanted to get some understanding of our open source community's activity on our GitHub repos. I was able to quickly configure GitHub to send webhooks to Zapier , which in turn posted the JSON to New Relic Insights.

Insights is schema-less and configuration-less - just start posting JSON key value pairs, then start querying your data.

Within minutes, data was flowing from GitHub to Insights, and I was building widgets on my Insights dashboard to help my colleagues visualize the activity of our open source community.

#GitHubAnalytics #OpenSourceCommunityAnalytics #CommunityAnalytics #RepoAnalytics

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.

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

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

We are evaluating an APM tool and would like to select between AppDynamics or Datadog. Our applications are largely hosted on Microsoft Azure but we would keep the option to move to AWS or Google Cloud Platform in the future.

In addition to core Azure services, we will be hosting other components - including MongoDB, Keycloak, PagerDuty, etc. Our applications are largely C# and React-based using frontend for Backend patterns and Azure API gateway. In addition, there are close to 50+ external services integrated using both REST and SOAP.

Hello People, I want suggestions about monitoring and alert tools to use with .NET application which will be hosted on Microsoft Azure. I have Azure Monitor,, Grafana, and Prometheus in my consideration. What would you suggest among these tools? If you have any other suggestions, please share the ideas. Thank you.

Can I get metrics available through Prometheus into Azure Monitor, specifically into log analytics? (VM'S). I am running a couple of VM's inside Azure portal and I have my own private besu nodes running on them. I have my metrics set up inside the Prometheus but I was hoping to hook it up securely to Grafana but I tried everything and I can't. So the next thing is to see if can I get the metrics available through Prometheus into azure monitor, specifically into log analytics. The aim is to get the sync status, and the highest block number on each node, into log analytics so we can see what each is doing. That way we know, on a quick look, the status of each node and by extension, the condition of the private chain. What worries me is that although I have alerts if blocks stop being created or nodes lose peers we cannot see it quickly.

Prometheus is one option to give us those stats. If we can get data from Prometheus into log analytics that would solve the problem.

Can anyone help me with how I can go about it or any links? All I am seeing is for containers but I want for my VMs.

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

Hi Folks,

I am trying to evaluate Site24x7 against AppDynamics, Dynatrace, and New Relic. Has anyone used Site24X7? If so, what are your opinions on the tool? I know that the license costs are very low compared to other tools in the market. Other than that, are there any major issues anyone has encountered using the tool itself?

I am designing a Django application for my organization which will be used as an internal tool. The infra team said that I will not be having SSH access to the production server and I will have to log all my backend application messages to Splunk. I have no knowledge of Splunk so the following are the approaches I am considering: Approach 1: Create an hourly cron job that uploads the server log file to some Splunk storage for later analysis. - Is this possible? Approach 2: Is it possible just to stream the logs to some splunk endpoint? (If yes, I feel network usage and communication overhead will be a pain-point for my application)

Is there any better or standard approach? Thanks in advance.

I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

Hi All, I am working upon developing a C# based windows service to work as a TCP server using Visual Studio 2019 as a development studio while using .NET Core 3.0.

As its a background application, therefore, I am up on to add tracing and logging feature into it and want to write to 'text file' my trace and log outputs.

By my research, I came upon 2 potential tracing and logging frameworks and on the verge to choose 1, therefore Serilog OR NLog.

Anyone here who can help me out by his/her experience of 1 framework being better than other.?