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You just launched your application and it’s attracted more users than you were expecting. Your web server is bombarded with data. Now you need to know more about your users: what is the dominant device they’re using, and how long are they staying on the app? A time series database will help you answer these questions. It allows you to save data for a given point over a specified period of time, which gives you insight into what type of usage you’re getting and when.

Are you sure your Hive queries are performing at their best? You might be surprised. Apache Hive is the most prevalent query engine used in many of the largest enterprise environments today, but that doesn’t mean it works optimally automatically. To get the most out of the engine and achieve Hive query optimization, it’s important to tune its performance. But before we dive into that, let’s cover the basics of Hive performance tuning. What is Hive performance tuning?

One of the great features of the InfluxData cloud platform is that it comes out of the box with all the tools you need to quickly read and write your data to the database. Here, we’ll walk through creating data visualizations with InfluxDB and plotly.js, a JavaScript graphing library built on top of d3.js and stack.gl.

Aiven for PostgreSQL® Temporal offers a microservice orchestration platform, and Aiven offers data infrastructure. Read why these two are practically made for each other!

Amazon Simple Storage Service (S3) is an essential cornerstone of AWS and among its most popular service offerings. S3 allows tenants to store, secure, and retrieve data from S3 buckets on demand. It is widely used for its high availability, scalability, and performance. It supports six storage classes and several use cases, including website hosting, backups, application data storage, and data lake storage. There are two primary components of Amazon S3: Buckets and Objects.

The attacks cybercriminals are using grow more complex and sophisticated by the day. However, with the help of big data analytics, you aren’t left defenseless. You can use big data analytics to fortify your cyber defenses and reduce your vulnerabilities. 1. Analytics can help you uncover suspicious patterns. Big data analytics protects your business data and strengthens your cybersecurity by providing valuable cybersecurity intelligence to your data analytics experts.

OpenSearch is a community-driven and truly open source search and analytics suite. Read on for a high-level look at its capabilities.

This article explores some of the ways that invalid data can be rejected when writing to InfluxDB and describes a new feature that makes it easier to know when and why that happens.

Knowing what business problem you want to address will help you know which data mining technique will produce the best results. Each of the data mining strategies listed below addresses a different business challenge and yields a different result. We are surrounded by big data in today’s digital world, which is expected to rise at a rate of 40% per year over the next decade. The irony is that we are awash in data yet deficient in knowledge. Why?

The FinTech revolution is truly transforming the way that we manage our money, bringing new online banks, new models for insurance provision, new ways to access loans, and much more. It rests on a number of pillars, but one of the most important is that of big data.