Dashbird
Lumigo
Epsagon
Datadog
NewRelic
AWS Lambda
Amazon CloudWatch
AWS X-Ray
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Databricks
Google Cloud Dataproc
Qubole
Snowflake
HortonWorks Data Platform
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
Dashbird Amazon EMRNo features have been listed yet.
Based on our record, Dashbird should be more popular than Amazon EMR. 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
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: about 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
Lumigo - With one-click distributed tracing, Lumigo lets developers effortlessly find and fix issues in serverless and microservices environments.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Epsagon - Track costs and fix your serverless application.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?