Operations | Monitoring | ITSM | DevOps | Cloud


Myth #4 of Apache Spark Optimization | Manual Tuning

Manual tuning can remediate some waste, but it doesn’t scale or address in-application waste. Watch this conversation to learn why manually tuning your Apache Spark applications is not the best approach to achieving optimization with price and performance in mind. Visit Pepperdata's page for information on real time, autonomous optimization for Apache Spark applications on Amazon EMR and EKS.

The Impact of Managed Services on Workflow Efficiency

Managed services are changing the way businesses deal with their operational requirements, from support in IT to managing networks fully. When companies assign necessary roles to managed services providers, they gain better work flow efficiency. This kind of change not just boosts daily productivity, but also strengthens a business's ability to adjust and expand effectively.

Unified Namespace and InfluxDB: Streamlining IIoT Operations for Industry 4

The Industrial Internet of Things (IIoT) has revolutionized the way industries operate, enabling businesses to collect and analyze data from their operations in real-time. However, managing and analyzing data from diverse sources can be a challenge. While sensors and systems may use the same transport protocols, the shape and type of data generated can vary from one device to another. A lack of uniform, clean data creates challenges and obstacles when it comes to getting timely insights.

Why Harnessing Hourly Granularity Can Optimize Cloud Savings

If you’re working in the cloud, you’re part of a rapidly growing industry. Global spending on public cloud services is set to double, reaching $482 billion in 2024, up from $243 billion in 2019, with a compound annual growth rate (CAGR) of 16.5% What’s the takeaway? With organizations increasingly depending on cloud services, managing costs effectively is a must. Otherwise, the expenses will pile up, and money will flow down.

Hot and cold data with Apache Kafka, Tiered Storage, and Iceberg

Utilizing the true potential of data streaming is key to business success. In this Data (R)evolution episode, we're joined by Josep Prat and Filip Yonov to dive into the transformative features of Apache Kafka and its evolving role in data architecture. They discuss the critical importance of collaboration and feedback in enhancing Kafka's capabilities, the future of "lake house" technology, exciting updates from the Open Source Program Office (OSPO), and the importance of Kafka's readiness to support evolving data formats—making it a backbone for modern data ecosystems.

Real World Software Development: Finding, Reproducing, and Fixing Bugs

Veteran developers and staff engineers at InfluxData, Nga Tran and Andrew Lamb, have an honest conversation about dealing with software bugs. Bugs can be frustrating, but they can also be thrilling. They are a sign that people are actually using your software - and that's a good thing! Andrew and Nga talk through a recent bug their team encountered, how they approached resolving the issue, and what considerations go into building a permanent fix.

Complete Guide to Azure VM: Pricing Models, Types & More

Trying to find the best virtual machine on the market that gives you the flexibility of easy scalability and the promise of a secure network – and doesn’t cost an arm and a leg (and maybe another arm)? Azure VM is likely the best solution for you… assuming you can project costs correctly. However, Azure doesn’t make it easy with its different offerings and pricing models.

Complete Azure SQL Pricing Guide

Azure Structured Query Language (SQL) has 18 different deployment options, service tiers, compute models, and two different pricing models: vCores and Database Transaction Units (DTU). Because of these complexities, it’s nearly impossible to project monthly budgets! This guide will explain the common Azure SQL pricing configurations and offer tips on optimizing your cloud budget.

Optimizing Space Technology: Fast Data Access with InfluxDB and Apache Parquet

To win the space race, aerospace and aviation companies must be fast. The end-to-end cycle of testing, visualizing test data, and making improvements demands swiftness, especially when a single launch yields billions of data points. It starts with real-time access to data. Real-time data analysis with nanosecond precision is crucial for monitoring environmental and habitat conditions when lives are at stake. Speeding up the iteration pipeline is essential but not sufficient. Cost efficiency matters too.

How to Design and Create Cloud-Native Applications for Data Analysis

Are you a data scientist, analyst, or IT professional seeking to leverage cloud computing for your data analysis projects? We understand your challenges in managing large datasets, scaling your analysis, and maintaining cost-effectiveness. This guide will walk you through designing and creating cloud-native applications for data analysis. By the end of this article, you'll have a clear roadmap for building robust, scalable, and efficient cloud-native data analysis applications that can transform your organization's data capabilities.