Analytics

crunchmetrics

Product Update: Smart Insights on Detected Anomalies

Over time, our customers have adopted and used our tool for detecting anomalies or discovering opportunities. We understand that the journey does not stop there. We still have to find the root cause of the incidents. To help our customers in their endeavor to investigate and reach closure, we now introduce Smart Insights on detected anomalies.

Snowflake

Snowflake combines the power of data warehousing, the flexibility of big data platforms and the elasticity of the cloud at a fraction of the cost of traditional solutions.

Fivetran

Fivetran fully automated connectors sync data from cloud applications, databases, event logs and more into your data warehouse.
influxdata

BIRCH for Anomaly Detection with InfluxDB

In this tutorial, we’ll use the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm from scikit-learn with the ADTK (Anomaly Detection Tool Kit) package to detect anomalous CPU behavior. We’ll use the InfluxDB 2.0 Python Client to query our data in InfluxDB 2.0 and return it as a Pandas DataFrame. This tutorial assumes that you have InfluxDB and Telegraf installed and configured on your local machine to gather CPU stats.

chaossearch

The 'No Data Movement' Movement

Organizations are building data lakes and bringing data together from many systems in raw format into these data lakes, hoping to process and extract differentiated value out of this data. Anyone familiar with trying to get value out of operational data, whether on prem or in the cloud, understands the inherent risks and costs associated with moving data from one environment to another.

logdna

Logging Best Practices Part 2: General Best Practices

Isn’t all logging pretty much the same? Logs appear by default, like magic, without any further intervention by teams other than simply starting a system… right? While logging may seem like simple magic, there’s a lot to consider. Logs don’t just automatically appear for all levels of your architecture, and any logs that do automatically appear probably don’t have all of the details that you need to successfully understand what a system is doing.

A Dose Of Data Science Demystification

Join two data engineers and analysts in pulling back the curtain on real customer engagements, showing how to select and implement advanced data science and analytic techniques. In this session we will discuss our implementation of two data science models at a large agricultural products manufacturer: a propensity-to-buy model and a recommendation engine. We will discuss how each of these models works and how they were implemented for our client.

Make Your Data Fabrics Work Better

To gain the full benefits of the DataOps strategy, your data lakes must change. The traditional concept of bringing all data to one place, whether on-premises or in the cloud, raises questions of timing, scale, organization and budget. The answer? Data fabric. It replaces traditional data lake organization concepts with a more flexible and economical architecture. In this session, we'll define what a data fabric is, show you how you can begin organizing around the concept, and discuss how to align it to your business objectives.