Snowflake integration

Project Configuration

To configure the connection to snowflake, you need the locator url from SnowFlake. The url can be obtained by going to ‘Admin’ -> ‘Accounts’
Copy the url for the Locator (can be copied by hovering over it). This will be used when creating project in InsightFinder

Project Creation

Go to ‘Settings’ -> ‘System Settings’ -> ‘Add New Project’ -> ‘All Categories’ . Search for and/or select “Snowflake performance monitoring” from the list
You will be redirected to the SnowFlake Project Creation Page in InsightFinder

Common Settings

Snowflake Username: username for the snowflake account
Database Address: The url copied from Snowflake Locator in Project Configuration from above
Database Password: The password for the snowflake account/database
Warehouse: COMPUTE_WH (Can use other warehouse if wanted)
Data Type: Metric (Default prefilled, leave it as it is)
Sampling Interval: 5 min (Default, can change is according to requirements)
- Project Name: Enter the desired project name
- System Name: Enter/Choose the system for which this project should be part of

Query Settings

Snowflake Self Monitoring

For monitoring Snowflake’s performance

Database Name: SNOWFLAKE (database name needs to be this since we want to monitor snowflake itself)
Query: Since we’re monitoring snowflake, we’ll be using the following queries:


select NAME as instance_name, START_TIME AS time_stamp[yyyy-MM-dd HH:mm:ss], CREDITS_USED_COMPUTE, CREDITS_USED_CLOUD_SERVICES, CREDITS_USED from ACCOUNT_USAGE.METERING_HISTORY;
select WAREHOUSE_NAME as instance_name, START_TIME AS time_stamp[yyyy-MM-dd HH:mm:ss], AVG_RUNNING, AVG_QUEUED_LOAD, AVG_QUEUED_PROVISIONING, AVG_BLOCKED from ACCOUNT_USAGE.WAREHOUSE_LOAD_HISTORY;

General Metric Monitoring

If you want to stream monitoring data from other systems that are stored in SnowFlake, you can write the query to fetch them from SnowFlake and forward to InsightFinder.

The requirements to setup the query are given below:

Database Name: Name of the database where the metric data is stored.
Query: Can write any query wanted that will return valid timestamp(s), instance name(s) and metrics (numerical values). Few important things to note:
- The column being used as the instance name needs to be returned as ‘instance_name’
- Timestamp values need to be reformatted to ‘yyyy-MM-dd HH:mm:ss’ format and renamed to ‘time_stamp’
- At Least one value returned from query needs to be a metric value (apart from timestamp and instance names)
- Need to provide schema as well as table when writing query
  - Example Query:


select NAME as instance_name, START_TIME AS time_stamp[yyyy-MM-dd HH:mm:ss], CREDITS_USED_COMPUTE, CREDITS_USED_CLOUD_SERVICES, CREDITS_USED from ACCOUNT_USAGE.METERING_HISTORY;

Finish Project Creation

Click on ‘Finish’ to create the project once all the above settings are filled. You will be redirected to the project settings page automatically once the project is created.

Check Data

Once the project is created and has collected data, the data can be viewed by going to the line chart page for the respective project

AWS Cloud

How InsightFinder uses AWS solutions to build an AI-driven predictive observability platform

May 17, 2023

Holding onto your time—to be a spouse, to be a community member, to be an individual—is difficult, particularly in the world of startups. InsightFinder, an artificial intelligence (AI) startup that uses machine learning (ML) to help customers prevent outages in their cloud infrastructure, is on a mission to change that. Founded by Helen Gu in 2016, InsightFinder uses unsupervised machine learning to make cloud infrastructure more reliable. The company’s AI-driven predictive observability platform helps companies to predict business-impacting incidents as well as pinpoint the root cause of impending incidents to avoid business loss and brand damage. Read more on the AWS startup blog here ……

How InsightFinder Can Help Amplify Your Confluent Data

April 10, 2023

In today’s digital age, developers need solutions that can help them quickly build and deploy applications. Confluent Cloud is an innovative way to streamline the application development process. But what if you could do more? What if you could amplify the data coming through your Confluent Kafka cluster with predictive capabilities? Enter InsightFinder. InsightFinder analyzes powerful metrics and log data from any user source and uses real-time streaming to analyze and predict performance issues. Read on to learn more about how InsightFinder can help you get the most out of your Confluent Cloud experience.Real-Time Analysis & Predictions InsightFinder's real-time analysis and predictions give developers better insights into their data streams in order to make more informed decisions about their applications. This helps developers identify potential problems before they become serious issues, saving time in the long run by avoiding costly fixes down the line. Additionally, InsightFinder's machine learning algorithms can detect anomalies in data streams that may not be immediately obvious to human eyes, helping developers stay ahead of future issues before they arise.Cloud Monitoring InsightFinder also provides cloud application monitoring capabilities that allow developers to monitor their cloud usage in real-time and identify trends as they happen. This allows developers to optimize their cloud resources for maximum efficiency while proactively preventing performance issues due to resource constraints or capacity limits. Additionally, InsightFinder can generate comprehensive reports with detailed metrics on application health and usage patterns so that teams can stay up-to-date on their cloud operations at all times. Confluent Cloud is a powerful tool for streamlining the application development process. When combined with InsightFinder’s predictive capabilities it becomes even more powerful by amplifying their Confluent data with real time analysis and predictions. Insightfinder gives developers the ability to get deeper insights into application performance while proactively preventing potential issues before they become serious problems. This makes it an invaluable addition for any developer leveraging Confluence Cloud for streamlined application development. To consider Confluent and InsightFinder, learn more here and sign up for a free trial here.

We have focused on responding to incidents when they happen, and that needs to change. The traditional role of IT Operations is to set up, fix, and monitor the applications and systems a particular organization needs. While the responsibilities include proactive work, it is understood that IT Operations is responsible for resolving issues that go wrong. As a result, the job has been focused on monitoring and response to problems. Incident response is the traditional action and role we think about when we define traditional ITOps roles.They are the firemen of the organization. An incident response organization places its primary importance on observability and alert tools. Which tools can detect and surface a problem the most quickly? How accurate is the alert tool? Is there a lot of noise, or false alarms, which causes incidents to be overlooked? The function of all these tools are meant to help you pay attention and respond quickly when something goes wrong. However, today’s reactionary response mentality for IT is no longer a sustainable strategy for businesses. Why is this the case? First, the architecture of applications has become increasingly complex. Operational challenges, such as CI/CD acceleration and microservices have created tooling complexity. Today, there are more interconnected systems, making it harder to have clear visibility into what is happening across the entire environment. When they go down, it can take hours and often days to find where and when the problem occurred. Many times, reactive analysis often fails to find the true root causes and the same incident is more than likely to happen again in the future. With increasing complexity comes the need to monitor the different systems and applications that are controlling your business. Observability needs have grown as well as tools to help create visibility within these systems. While more information can be good, it comes at a cost. With new data comes data silos. Although more insights are available, they most often exist in silos. Therefore, when a problem occurs and is flagged in one system, it takes time to understand how the problem impacts all systems, how to untangle it, and where it started. Businesses need to use a tool that has overall visibility to all tools and applications, and understand how they work together. By leveraging the right AI system to oversee and analyze all the data and information, businesses can now be proactive because their data can be used to predict incidents. The new approach, or proactive ITOps, unites and analyzes all data sources, and therefore can detect root causes before they progress into incidents. Because root causes are flagged at an early stage, the incident can be predicted instead of observed when it happens. As a result, businesses can fix problems in advance of them taking a huge impact on the bottom line. Monitoring, by definition, is reactive. Contextualization, correlation, and analytics are needed in order to make use of the massive amount of data and observability information now available. Once these elements are applied to massive amounts of information, you can be proactive about your ITOps focus.

Explore InsightFinder AI

Take InsightFinder AI for a no-obligation test drive. We’ll provide you with a detailed report on your outages to uncover what could have been prevented.