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.

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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.

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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.

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HPCC Systems&reg: 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.

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Taming the Data Lake: The HPCC Systems Open Source Big Data Platform

A “Data Lake” is an architecture and methodology for the continuous management of complex data that stores data on raw format for increased agility on data exploration. As it enters the lake, each piece of data is readily available for manipulations and insights via a unique identifier and a set of extended metadata tags. In contrast, a “Data Warehouse” stores data in a predefined format for faster delivery of data analysis results.

HPCC Systems offers the best of both worlds by combining the fast performance of a Data Warehouse for information delivery with the ability to treat data as if it were in a Data Lake when it comes to data exploration. HPCC Systems uses distributed data architecture and a parallel processing methodology in order to work with large datasets. Enterprises are adopting data lake technology to manage their rapidly growing internal datasets and to solve complex problems through data analysis to improve their relationships with customers and suppliers.

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The HIDDEN ROI of Embedded Analytics for SaaS Companies

As a SaaS leader, you know that end-user expectations encompass a seamless user experience, easy to use self-service analytics, and immediate data response times.

Delivering these features requires an embedded analytics solution with a different approach. One that includes both the user facing components and the data management layer to ensure the solution can scale and meet the custom needs of SaaS end customers.

Read this brief to learn FOUR areas for cost savings and FOUR monetization opportunities with embedded analytics that SaaS leaders need to know about.

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How Embedded Analytics Helps Product Managers Exceed Their KPIs

PMs are the CEOs of their product. This challenging role requires people management, expectation management, and delivering business results. In a SaaS environment, the challenge is compounded as results are measured daily, monthly and quarterly.

However, new vendors have emerged to assist Product Managers in delivering cutting edge experiences to their end-users which align to important KPIs. Below are five ways embedded analytics help PMs exceed their key performance indicators.

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A Product Manager’s Guide to Embedded Analytics

Our Embedded Analytics Product Fit Guide will walk you through the process of choosing an analytics provider and outline all of the steps involved. It was designed as a conversation starter to get you thinking about your specific needs, so you can align your requirements and priorities and ensure your analytics solution is a perfect match for your needs.

There are many considerations you should be thinking about as you evaluate different analytics vendors and solutions. It is important to both understand your needs and specific requirements as well as know which of them are the most important priorities for your project.

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Build vs. Buy: Embedded Analytics for SaaS Providers

As a SaaS leader, you know that the more metrics, insights, and analytics you add to your products, the more engagement you’ll have and the stickier your product will become with customers.

At what point do you decide to keep building your own in-house analytics or invest in an analytics platform that can be easily embedded into your software?

Read our Build vs. Buy Analytics eBook to learn:

  • Top 4 benefits of embedded analytics.
  • A quick cost comparison of in-house vs embedded analytics.
  • 10 considerations to help your company deliver the RIGHT analytics solution for your CUSTOMER needs.

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All Digital 3 Evaluation Checklist

Selecting an ELN (Electronic Lab Notebook) for your lab can be overwhelming. This checklist will help you gather information and make the necessary decisions to move into the next phase of choosing an ELN system.

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NEOsphere Streamlines Experimentation Process With eLabJournal

NEOsphere strives to become the preferred proteomics partner of pharmaceutical and biotechnology companies active in the TPD space to expand their programs and create new entry points for drug discovery.

To streamline their documentation process and track samples more efficiently, the NEOsphere team searched for a customisable solution that fit their unique needs. They needed an online solution to scale up their documentation and enable them to better manage their increased samples throughout the entire experimentation process.

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eLabNext Enables Internal COVID-19 lab for Boston University

Boston University (BU) was able to establish an in-house COVID-19 testing lab for its students, faculty, and staff with the help of eLabNext solutions. Despite the challenge of integrating two separate EMR systems and testing robots, eLabNext's robust APIs played a critical role in enabling the lab to process over 9,000 samples at its peak. This case study showcases how eLabNext facilitated BU's testing objectives by providing a streamlined and efficient approach. If you're interested in integrating custom lab tools and equipment like BU, download the case study to learn more.

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Bringing ‘All Digital’ to Your Lab

Interested in learning about the transition from paper to digital in the lab? Download our white paper.

Key Points:

  • Life science labs across academia, industry and government are producing, storing, analyzing and sharing a massive amount of digital data.
  • Yet, many researchers still rely on paper lab notebooks that don’t have the capacity, formatting or sharing capabilities to accommodate or integrate digital data.
  • An all-digital approach using an electronic lab notebook (ELN) can solve these issues through improved searchability, time-saving functionality, decreased data entry errors, and more.
  • eLabJournal is an intuitive, flexible, all-in-one ELN that improves lab efficiency when documenting, organizing, searching, and archiving data, samples, and protocols.

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