Dashbird is an observability, debugging, and intelligence platform designed specifically to help serverless developers build, operate, improve, and scale their modern cloud applications on AWS environment fast, securely, and with ease. It’s free to use for up to 1M invocations and doesn’t require any code changes.
Dashbird fills the gaps left by CloudWatch and other traditional monitoring tools by offering enhanced out-of-the-box monitoring, operations, and actionable insights tools for architectural improvements, all in one place.
Full observability covered for AWS services: Lambda, API Gateway, DynamoDB, SQS, ECS, Step Functions, Kinesis, HTTP API Gateway, RDS, SNS, OpenSearch, ELB.
Dashbird’s approach is fairly simple, all the mission-critical data of your entire serverless system is placed in a single dashboard giving you a birds-eye-view of the entire system activity. Moreover, you get immediate alerts on any errors or warnings that may arise and get pointed to the exact point of failure in the system so it can be resolved fast.
The 3 core pillars of Dashbird are:
Real-time end-to-end serverless observability Automatic Failure Detection Continuous Well-Architected reports on your entire infrastructure
No features have been listed yet.
Based on our record, Dashbird should be more popular than Google Cloud Dataflow. It has been mentiond 59 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
There's more to come at Dashbird, as we're already building more features to help you run the best possible AppSync endpoints. This includes a set of well-architected insights to guide you with best practices. - Source: dev.to / over 1 year ago
Observability in serverless Tools like Datadog, Splunk, Thundra.io, New Relic, and Dashbird make monitoring and debugging serverless applications easy. They collect metrics, logs, and traces from AWS Cloudwatch and X-ray. - Source: dev.to / almost 2 years ago
With its latest release, Dashbird added support for APIG's HTTP APIs. All your HTTP APIs are automatically monitored after installing Dashbird into your AWS account. You need to deploy a CloudFormation template to set up Dashbird integration; it doesn't require any code changes! - Source: dev.to / almost 2 years ago
I decided to try out Dashbird because it’s free and seems promising. They’re not asking for a credit card either, making it a “why not try it out” situation. - Source: dev.to / about 2 years ago
With the emergence of managed and distributed services, the monitoring landscape will have to go through a significant change to keep up with modern cloud applications. Currently, devops overhead is one of the biggest obstacles for companies looking to use serverless in production and rely on it for mission-critical applications. Our team at Dashbird is hoping to solve that one problem at a time. - Source: dev.to / about 2 years ago
Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 1 year ago
This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 1 year ago
I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 1 year ago
You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 1 year ago
It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 2 years ago
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