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All Research Sponsored By:Dremio

  • Dremio Architecture Guide

    While data lakes are booming in popularity thanks to their usefulness in storing large amounts of unstructured data, businesses often find themselves building extensive data architectures so they can organize that data. Read this white paper to learn how to use a powerful data lake engine like Dremio’s and skip the expensive infrastructure.

  • The Rise of the Cloud Data Lake Engine: Architecting for Real-Time Queries

    As cloud data lakes have become more and more popular, a similar number of cloud data lake engine solutions have popped up around them. But what is a data lake engine? Check out this webinar featuring the Eckerson Group to learn about the rise of the cloud data lake engine and how it could benefit your company.

  • Ensuring an Open Data Lake Future

    One of the advantages of building a data lake is that your organization has the potential to keep your basic data architecture the same, even as you switch BI and analytics tools around. Read on to learn how to avoid data lake vendor lock-in and ensure your data lake can serve your organization for years using its open architecture.

  • Reducing the Cost of Cloud Data Analytics: 3 Architecture Choices

    As IT budgets shrink due to economic uncertainty, analytics demand expands as businesses try to make sense of that very same uncertainty. So how can your business reduce cloud data science costs while still getting the results you need? Check out this white paper by Dremio to learn 3 architecture choices to reduce your cloud analytics costs.

  • Building an Efficient Data Architecture for Maximum Productivity

    Productivity is perhaps best understood as a combination of efficiency and effectiveness: doing the right things at a minimal cost. Watch this webinar to learn how you can use a cloud data lake to create a data architecture that is as productive as possible.

  • The Rise of the Cloud Data Lake Engine

    A new category of analytics platform has evolved. This guide walks you through the top takeaways and recommendations for the cloud data lake engine, and explores how it can positively impact your business.

  • Best Practices for Efficient and Productive Analytics

    Access this white paper to learn Dremio’s best practices approach to organizing a semantic layer to enable interactive performance for business users and get guidance on designing and implementing the logical semantic layer.

  • Optimize Your Cloud Data Lake Query Performance and Economics

    Inefficient query workload management creates poor performance and slows down your cloud data lake. Watch this Dremio webinar to learn how you can optimize your data lake query performance and reduce wasteful spending in your cloud infrastructure.

  • Best Practices for Building a Fast and Reliable IoT Data Pipeline

    The Internet of Things, or IoT, is the future of many enterprise data systems. Everyone from retailers to manufacturers can benefit from a highly intelligent, interconnected data system. Check out this webinar to learn how to build that data system in the cloud, using analytics and machine learning as your insight engines.

  • Building a Best-in-Class Data Lake

    Cloud data lakes are a natural fit for businesses seeking scalable infrastructure, ready built for machine learning and big data challenges. But there are a variety of challenges associated with building them, including cost control, data governance, security and latency issues. Check out this webinar to learn how you can resolve these issues.

  • 11 Best Practices for Migrating to a Cloud Data Lake

    The business case for cloud data lakes is already clear: scaling finances, flexible storage options, and better performance make it a natural fit in most business plans. But migrating to this data lake is another question entirely. Check out this Dremio white paper to learn 11 best practices for migrating to a cloud data lake.

  • 3 Steps for Making High-Performance BI

    Data lakes are a cost-effective, easy way to store data. But taking that data and using it for BI or analytics purposes isn’t always easy; it’s stored in a way that often requires something like an ETL system to prepare that data for use. Learn how you can reduce data pipeline complexity and empower your data consumers in this Dremio white paper.

  • How a Self-Service Semantic Layer for Your Data Lake Saves You Money

    Building a data lake is a step in the right direction when modernizing data infrastructure, but it’s not the only step necessary towards making your big data sets useful. Watch this webinar to learn how to build a self-service semantic layer that lets your analysts and engineers manage their own projects without relying on IT—freeing them for more.

  • Top 5 Data Science Industry Predictions for 2020

    The new decade promises to be a big one for new technology developments; what, exactly, does 2020 hold for the data science industry? Watch this webinar to learn 5 predictions for data science in 2020, including changes to the IoT, big data architecture, cloud data lakes and warehouses, and more.

  • Unlocking Data Science on the Data Lake using Dremio, NLTK and Spacy

    A quality data pipeline, one that is able to access all of your information in disparate sources, can give your data scientists a holistic view of the data instantly—giving them more time to analyze it. Read this resource to see how Dremio can empower your business to build your own pipeline to streamline data access from a variety of sources.

  • BI On Big Data: What Are Your Options?

    The advent of big data changed data analytics, but not necessarily for the better. Right now, companies often struggle to turn the massive amounts of data that they’ve collected into useful, unified data sets. Download this ebook by Dremio to examine some of the challenges and trends in the data management world right now.

  • Dremio Architecture Guide

    Having quality data architecture can allow your organization to stop managing your data and start using it, but achieving this is often easier said than done. Read this Dremio architecture guide to learn how their data lake engine can help you easily navigate and use your data lake.

  • Self-Service Analytics

    For today’s organizations, the complexity of data, the speed with which it changes, and the massive amounts of it make modern data analysis an extremely complex, IT-dependent task. Read this white paper by CITO Research, sponsored by Dremio, and see how you can free up your IT staff while empowering your employees.

  • Dremio Security Architecture Guide

    Because data sets and analytics systems are valuable targets for cybercriminals, it is important that your data access and analysis systems provide a variety of security options so that you can protect your organization’s data from potential threats. Read the attached guide to see some security choices that the data lake engine Dremio offers.

  • Vectorized Query Processing Using Apache Arrow

    Apache Arrow is popular for many reasons: it can interact with a variety of languages, is an open source project, and facilities speed within your database. It is a versatile tool that can be used to maximize hardware functionality and utilize vectorized query processing.Watch this webinar to learn more about vectorized query processing.

  • Dremio Architecture Guide

    Most organizations deposit their raw data in data lakes, where it can be stored easily. But it can require a complex series of programs, tasks, and pipelines to leverage this data into analytics insights.Read this guide to learn how Dremio can use your data lake and allow your business to derive analytics insights directly from your data.

  • Analyzing Multiple Stream Data Sources using Dremio and Python

    Data processing is a complex series of tasks – data of different kinds and formats need to be aggregated into a single database that easily accessible to users across your organization. Read this tutorial to learn how Dremio can unify data from disparate sources into a single dataset, ready to be utilized by an analytics or data science program.

  • Using Data-driven Permissions to Secure Your Data Lake

    When managing a data lake, it is important to make sure that this data is secured for a variety of reasons.How does your business implement data lake security features? Additionally, how customizable are these security tools?Read this white paper and learn how you can create your own data lake permissions with Dremio.

  • Running SQL Based Workloads In The Cloud

    Having a data lake engine that’s flexible enough to handle a variety of workloads in an setting is important to a business like yours. Watch this webinar to learn about some of the specific applications of Dremio and learn how it can handle various SQL-workloads in the cloud.

  • Using a Data Lake Engine to Create a Scalable Data Pipeline

    Your organization should have a data pipeline that can access all of your data stored across one or more disparate sources. But how do you know if your pipeline is efficient and cost-effective? Watch this webcast to learn how you can create your own, scalable data pipeline using the data lake engine Dremio.

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