In this contributed article, Mayank Mehra, head of product management at Modak, shares the importance of incorporating effective data observability practices to equip data and analytics leaders with essential insights into the health of their data stacks. Mayank also explains why this is becoming increasingly paramount, given the current trend towards modern, complex, and distributed data infrastructures.
Splunk Introduces New AI Offerings to Accelerate Detection, Investigation and Response Across Security and Observability
Splunk Inc. (NASDAQ: SPLK), the cybersecurity and observability leader, today announced Splunk AI, a collection of new AI-powered offerings to enhance its unified security and observability platform. Splunk AI combines automation with human-in-the-loop experiences, so organizations can drive faster detection, investigation and response while controlling how AI is applied to their data. Leaning into its lineage of data visibility and years of innovation in AI and machine learning (ML), Splunk continues to enrich the customer experience by delivering domain-specific insights through its AI capabilities for security and observability.
Busting Data Observability Myths
In this sponsored article, Rohit Choudhary, co-founder and CEO of Acceldata, breaks down four common myths and misconceptions around observability. In today’s economic climate, many companies are tightening their belts. They need solutions that help them run their business efficiently, smoothly, and reliably in order to maximize impact and keep customers happy. Data is every company’s most valuable asset, and data observability tools are indispensable for keeping an eye on data health and ensuring business continuity.
Modern Observability: When “Small” Data Beats Big Data
In this contributed article, Ozan Unlu, CEO and Founder of Edge Delta, explores how a cloud-first world demands that observability be approached in a different way, one that favors “small data” over “Big Data.” In some cases, Ozan believes, a central repository is no longer even needed.
Why SLIs and SLOs Are Essential for Observability
In this contributed article, Jemiah Sius, Director, Product Management, New Relic, discusses the difference between good and bad SLIs — and how that can inform creating the best SLOs to measure improvement. Establishing SLIs and SLOs will result in a simpler and more responsive observability practice, tighter alignment with the business, and a faster path to improvement. It’s simple and easy to get started, practice this on one service and see how well it works.
Grafana Labs Observability Survey 2023 Finds Centralization Saves Time and Money for an Industry Plagued by Tool and Data Source Overload
Grafana Labs, the company behind the open and composable operational dashboards, announced the findings of the Grafana Labs Observability Survey 2023. The report, which focused on the state of observability, found that organizations are challenged by tool sprawl and data source overload, with 52% of respondents reporting that their companies use 6 or more observability tools, including 11% that use 16 or more.
Slidecast: Ashwin Rajeeva, Co-founder & CTO of Acceldata Discusses Data Observability
In this slidecast presentation, Ashwin Rajeev from Acceldata describes the company’s data observability solutions. Acceldata solutions allow you to gain comprehensive insights into your data stack to improve data and pipeline reliability, platform performance, and spend efficiency.
Video Highlights: Why Does Observability Matter?
Why does observability matter? Isn’t observability just a fancier word for monitoring? Observability has become a buzz word in the big data space. It’s thrown around so often, it can be easy to forget what it even really means. In this video presentation, our friends over at Pepperdata provide some important insights into this this technology that’s growing in popularity.
How to Optimize the Modern Data Stack with Enterprise Data Observability
In this sponsored post, our friends over at Acceldata examine how in their attempt to overcome various challenges and optimize for data success, organizations across all stages of the data journey are turning to data observability where they can get a continuous, comprehensive, and multidimensional view into all enterprise data activity. It’s a critical aspect of optimizing the modern data stack, as we’ll see.
Data Quality Should Keep You Up at Night (But There’s an Antidote to Data-Induced Insomnia)
In this sponsored post, our friends over at Acceldata examine how integrating data observability into your business operations will create the necessary environment and feedback loop needed to improve data quality, at scale, on an ongoing basis. It will also help your enterprise make the most out of all the data quality best practices your data team adopts, and will also probably enable you to get a peaceful night’s sleep.