Cut Your Total Cost of Ownership By At Least 68% With inCloud Observability
When assessing the 'build or buy' observability dilemma, there are a number of factors to weigh out. Above all else, it's crucial to consider: ease of use, simplicity of installation, seamless integration with your existing stack, user adoption, and of course - cost of ownership.
The following is a detailed breakdown of all of the factors which impact the total cost of ownership, guiding you to an informed decision of whether to invest in a homegrown solution, or which existing solution to opt for off the shelf.
Monitor Everything You Run in The Cloud, With Zero Compromises
Using best-of-breed technologies, groundcover’s cloud control plane remains separate from your in-cloud data plane, keeping all observability data inside your cloud environment, at all times.
The result:
- Volume-agnostic pricing
- End-to-end coverage
- Full data privacy
- Total data retention control
onPrem and airGapped
In a cloud-centric world, data privacy and security are paramount concerns for businesses across all sectors. The challenge lies in effectively monitoring cloud environments while ensuring sensitive data remains protected, preventing exposure to external online components and third-parties.
Groundcover’s proprietary eBPF sensor and unique inCloud infrastructure enable all of your observability data (logs, metrics, traces, etc.) to remain in your environment at all times.
ELN Selection Template
Download NEUWAY Pharma’s guide for selecting the best electronic lab notebook software for their needs and adapt it to suit your own.
Rethinking the Role of ELNs – Are we making things worse?
In this talk, Brendan McCorkle, CEO of SciNote, explored the role of ELNs in data management, and asked the question - are we making things worse?
What is SciNote? We are a lab digitalization company with the mission to help humanity benefit the most from science and to preserve research data for future generations. Our electronic lab notebook (ELN) helps labs manage experiments, data, and teams, allows inventory tracking, and supports 21 CFR Part 11 and GLP compliance. It is the chosen solution by researchers at the FDA, USDA, and 100+K researchers in over 100 countries.
Functionalities Overview SciNote for Industry Labs
Download this document to learn more about team onboarding, data structure, efficient inventory tracking and management, streamlined protocol and SOP management, seamless team collaboration, compliance with CFR 21 Part 11 and GxP regulations, versatile integrations, and API access.
Staying Ahead of the Shuffle: 5 Keys to Lab Inventory Tracking in Ever-Changing Environments
Have you ever encountered the frustration of being unable to replicate an experiment due to the lack of traceability in your inventory items? Or perhaps faced the challenge of maintaining data continuity when students or staff members depart, taking crucial inventory information with them? These scenarios are all too common, underscoring the vital importance of inventory tracking and traceability in research.
Join us in our webinar, as we discuss 5 crucial inventory management strategies that can directly prevent the loss of vital experimental information, ensuring your research endeavors remain on track. Plus, discover the impact of integrating an Electronic Lab Notebook (ELN) with your inventory management system through real-world use cases. See firsthand how this integration, or utilizing an ELN with an embedded inventory management system, can bridge the gaps, keeping your research data accessible, comprehensible, and fully traceable—even years after conducting the experiment.
Unleashing the Power of AI in Biotech: 5 Actionable Insights from Ganymede, Snthesis, and SciNote
The digital transformation of the field of biotech R&D is accelerating, driven by the integration of artificial intelligence (AI) and machine learning (ML). These technologies are reshaping the way biotech companies approach, analyze, and interpret vast amounts of data, paving the way for more informed decision-making and innovative breakthroughs.
However, as AI innovation has been happening so fast, what actions can companies take to keep on top of it all?
In this webinar, CEOs from SciNote, Ganymede, and Snthesis discuss the current landscape of AI use in biotech, best practices and platforms that your team can leverage right now to ready your lab and data for AI/ML, and our predictions of how AI/ML will develop in the biotech industry in the future.
SciNote Electronic Lab Notebook
SciNote is a cloud-based ELN software with lab inventory, compliance, & team management tools used by the FDA, USDA and scientists in 100+ countries.
SciNote’s flexibility allows you to organize all your data in your preferred way. It gives structure and context to all your notes, excel sheets, tables, checklists, or pictures.
Organize your work by projects, experiments, and tasks. Write notes, assign inventory items, add a protocol, and enjoy checking off the protocol steps you’ve completed.
7 Steps to Manage Your Research Data Digitally
Well-organized data will help you stay on top of your R&D plan, and optimize the potential of your data – from securing funding to developing collaboration.
But, how do you get started?
In this webinar, we will walk you through 7 actionable steps that will help you begin managing your data digitally and keep your data organized. We will discuss FAIR, the guiding principle of scientific data management and sharing. Lastly, we will compare common ways of recording and storing data, and discuss their pros and cons.
This webinar is open to anyone interested in managing research data digitally. We will focus on the strategies to get you started, regardless of what system you will be using. You are welcome to share information about this webinar with anyone you think might be interested.
When a Snowflake Turns into a Blizzard
12 Reasons Snowflake Costs Get Out Of Control
For many teams, Snowflake's data warehousing capabilities seem limitless at first. But as usage grows more sophisticated, costs can balloon if not kept carefully in check. When Snowflake bills start arriving much larger than expected, it's time to tame the proverbial blizzard before it buries the budget.
This ebook focuses on twelve common culprits that can cause Snowflake costs to rise unexpectedly and a practical solution specific to analytics. From inefficient queries to oversized warehouses to unexpected data growth, these cost creep issues often happen gradually beneath the surface. But their collective impact can add up to a budget-busting blizzard if not addressed.
Top 5 Challenges in Designing a Data Warehouse for Multi-Tenant Analytics
If you don’t properly design your data warehouse for multi-tenant analytics, you’re gonna have a bad time. Most data warehouses are built to efficiently store large volumes of data from numerous sources, not for end-users of SaaS platforms working with multi-tenant analytics. This architecture mismatch causes a variety of headaches. We’ll outline the top 5 challenges and explain how to have a good time instead.
Keep the TCO of Your Big Data Platform Under Control with HPCC Systems®
In today’s enterprise, a successful big data strategy can mean the difference between success and failure. For example, Netflix reports the company is able to save $1 billion a year from customer retention thanks to its use of big data analytics, and enterprises in every other vertical market are following suit. Market research firm Statista forecasts big data analytics software spending will hit $68 billion by 2025. But adopting a big data strategy is a big undertaking for enterprises, and there are a host of questions an IT team must answer before they can decide on the best big data platform for their needs.
This paper will examine multiple criteria an enterprise IT team should consider before they adopt any big data platform. By rigorously evaluating a potential platform in each of these categories, IT teams will have a better understanding of the total cost of ownership (TCO) of their chosen platform. The paper will then apply each of those criteria to reveal how well an HPCC Systems data lake platform addresses the criteria. Finally, the paper will examine how an actual HPCC Systems customer evaluated HPCC Systems TCO and decided the platform was the best fit for their big data needs.
Understanding HPCC Systems® and Spark – A Comparative Analysis
Since its beginning, HPCC Systems has given its users a platform consisting of a single homogenous data pipeline. This significantly minimizes the amount of effort users spend on platform management, installation, and maintenance. Perfect for both data lakes and warehouses, HPCC Systems is extremely capable and efficient in processing large amounts of data due to an architectural design that leverages two specialized clusters, named Thor and Roxie, to manage and optimize the platform’s various functions.
This paper serves as a comparison between the architectures and feature support of Spark and HPCC Systems in regard to data lake capabilities.
HPCC Systems®: The End-to-End Data Lake Management Solution
Today, most organizations recognize that data is key to the ability to innovate and remain competitive in a rapidly changing business landscape. A key challenge:
As datasets become larger and more complex, it’s impossible to quickly respond to changing business needs using traditional relational data store such as data warehouse.
To overcome this challenge, many organizations — including some of the world’s largest companies — are successfully using a proven alternative approach: a data lake. Data lakes support datasets that are extremely large, complex and diverse, and they easily accommodate new data sources such as IoT. They allow IT groups to quickly create new applications that support changing business needs, unlocking the power of complex data for all users within the organization. They also scale much more easily and cost-effectively than relational databases. As a result, data lakes enable greater responsiveness to business groups and external customers, reduced costs, and greater scalability.
