Compare Tableau Prep with Datameer Spectrum and learn how Spectrum delivers the same ease of use in an enterprise-wide data preparation hub that covers more use cases and works with all your tools.
Datameer Spotlight is the control that data engineers require and the self-reliance and flexibility that data consumers crave.
With Datameer Spotlight you get all the capabilities and benefits of a distributed query engine like Starburst Data, with true self-service tools and a collaborative data catalog. Learn more.
Learn how Datameer Spotlight and AtScale compare and see how Spotlight facilitates faster analytics using more of your data.
Learn how Spotlight compares to Dremio and provides faster analytics of any kind on any type of data.
Learn how Spotlight compares to how Looker models and manages data, and see how it can accelerate and broaden your Looker analytics.
Learn how Spotlight compares to Tableau Data Management and see how it can accelerate your Tableau analytics.
Learn how Spectrum compares with Fivetran for data integration capabilities and see the hidden costs behind Fivetran.
Learn how Spectrum compares with Informatica for data integration capabilities, user experience, and pricing/packaging.
Radiant Advisors, a leading industry advisory firm, evaluated Datameer Spotlight and found it to be an accelerator of agile analytics and self-service data enablement.
Learn what ETL, ELT, and data integration are all about and how Datameer Spectrum handles them all.
See what data governance is, the role it plays in your organizations, and how Spotlight provides key data governance features to support your data operations.
Learn how National Instruments selected Datameer to identify and overcome various challenges on their journey.
We chatted with Priyanka Khosla, People Insights & Workforce Planning at Sobeys Inc. We discussed people analytics, key metrics, and best practices on how to recruit and hire essential workers during a pandemic efficiently. Watch this webinar to learn more.
Learn the details and underpinnings of the Datameer Spotlight architecture and key capabilities and see how Spotlight works.
Organizations are adopting cloud analytics in various ways, including public cloud, private cloud, multi-cloud, and hybrid cloud. And that adoption is taking place across a variety of use cases and in many industries. Managing data for cloud analytics continues to have its challenges. Ad-hoc Analytics powers faster delivery in the cloud.
Collaborative analytics increases knowledge around data, facilitates greater trust, brings new ideas on how to use the analytics, and generally produces faster insights with greater detail and accuracy.
Both data preparation and feature engineering are the most time-consuming and important processes in data mining.
Business, revenue, and cost benefits from faster insights and collaborative analytics.
As if managing data of increasing size weren’t hard enough, organizations are now challenged to monitor business processes, assemble complete views of customers, and weave a cohesive analysis of corporate performance based on hybrid data that are strewn across the traditional enterprise and multiple clouds.
Spotlight has AI-augmented “prep” features to sharpen up data for an analysis – blends, filters, simple modeling, and even JSON transformations.
Datameer Spotlight is highly complementary to and interoperable with enterprise data catalogs.
Businesses perform data profiling to better understand the condition and the value of their data, making it discoverable and actionable along the way.
Data mining provides several techniques that can help organizations classify this data and try to find patterns or relationships between pieces of data.
In the age of agility, businesses need a data management solution that can keep pace with rapid growth, enabling them to make sense of their data quickly and logically.
Enterprise data management is impossible if you can’t manage, access, and process large amounts of data.
AWS Datameer Customer Case Study – PrivacyMaxx
AWS Datameer Customer Case Study – AI Consumer Debt
Datasheet Spectrum for Microsoft Azure and PowerBI
As if managing data of increasing size weren’t hard enough, organizations are challenged to monitor business processes, assemble complete views of customers, and weave a cohesive analysis of corporate performance based on hybrid data strewn across the traditional enterprise and multiple clouds.
AWS Datameer Customer Case Study – Retailer
AWS Datameer Customer Case Study – Investment Bank
Agile Data Pipelines for Cloud Machine Learning solution brief
Agile Data Pipelines for Cloud Data Warehouses Solution Brief
451 Research Business Information Brief
Join us in the chat with our guest Patrick McGrath, Director of Product Management, Search & Analytics at Commvault.
Join Nikhil Kumar and Steve Egan while looking at data from 3 different drug clinical trial data, add patients’ demographics and comorbidity data, and solve which drug might be the most effective against Covid-19.
Join this webinar to learn how a financial firm used Spotlight to access hard to get data and bring it all together ‘virtually.’
Join our webinar to find out how one of America’s top auto-insurer used Spotlight to create a virtual analytics hub that enabled data scientists to quickly and easily find, gain access, and run advanced analytics on their disparate data landscape.
Join Frank Henze on how to migrate some or all of your analytics and data science workloads to the cloud with ease while also enabling teams to run their data science projects across hybrid and multi-cloud landscapes.
This webinar is for AWS users and administrators passionate about making their data in Amazon Redshift, RDS, s3 buckets, etc., easily accessible and consumable to analytics professionals across the organization.
Building knowledge and trust in data and analytics increase data literacy and accelerates analytics cycles. Join us for this webinar to learn the different methods and best practices to share knowledge, increase collaboration, and build trust in data and analytics.
By using agile analytics and data virtualization, your Snowflake projects are faster, more cost-effective, and less risky. This short webinar will discuss the core pillars that will make your Snowflake analytics projects truly agile.
Join us in a discussion with Andrew Brust. He advises customers on analytics strategies, writes about Big Data for ZDNet and GigaOm, co-chairs a series of developer conferences the Visual Studio Live!, and is an influencer in the Microsoft ecosystem, recognized as both a Microsoft Regional Director and Data Platform MVP.
We have a guest speaker David Menninger, SVP & Research Director of Data and Analytics Research at Ventana Research, to discuss collaborative analytics.
Join John Morrell and Steve Egan for this empowering webinar on how to gain faster analytics for your business, more excellent analytics teams’ productivity and more significant ROI on analytics initiatives.
In discussion with Gary Angel, CEO of Digital Mortar and long-time retail digital analytics thought-leader, we discuss retail industry analytics and its challenges. Watch this webinar to hear more.
There are many tools on the market that help with pieces of an analytics architecture, including cloud providers. Let’s sort through those options and explore ways to simplify your architecture to make it more agile and deliver analytics faster and easier.
While each use case may be unique, many common design patterns can be used for data preparation in data pipelines that curate data for analysis. Let’s explore these and see how you can apply them.
Learn how you can use advanced data preparation and exploration to explore your data to determine fit, rapidly shape your data for AI and ML engines, and deploy data pipelines to create a cooperative workflow with AI and ML tools.
Join our guest Gaurav Mishra, Director of Data Science at BMO Financial Group, discussing how a simple unified data platform will empower end-users.
Data collaboration is a complex, still-evolving category that has not yet reached maturity or gained critical mindshare in the industry while serving a real need in an organization.
Organizations that use Looker rely on Datameer’s self-reliant solutions for cataloging, access, governance, and collaboration – to make sense of your complex data landscape and rapidly build new analytics for use in Looker without risk.
Organizations that use the AWS ecosystem rely on Datameer’s self-service solutions to pipeline data to the cloud faster, search and perform petabyte-scale analytics across complex data landscapes, and unlock more insights from all their data.
With Datameer and Google Cloud Platform, analytic teams are completely self-reliant. Teams discover data, analyze it in minutes, and invest their time in what they do best – on the data-driven decisions that advance your business.
Organizations that use the AWS ecosystem rely on Datameer’s self-reliant solutions to pipeline data to the cloud faster, search and perform petabyte-scale analytics across complex data landscapes, and unlock more insights from all their data.
Data teams that use Snowflake harness Datameer’s self-reliant solutions for agile analytics across complex data landscapes to rapidly unlock 30x more insights from all their data and lower the costs of using Snowflake.
Access data for your Tableau dashboards that you never could before, including Facebook Ads, Workday, Twitter Ads, Hubspot, Shopify, Dynamics, SAP Concur, and so much more. No data warehouse necessary. No data engineering is required.
Join Bob Page, Datameer Spotlight Chief Product Officer, and Steve Egan, our Data & Analytics Solutions Engineer, on how to empower your analytics team to discover, and find any data in the enterprise, collaborate, and perform ad-hoc analytics with any BI or data science tool.
The right tools can enable a company to move quickly from regulatory compliance success to strategic data excellence in months. Datameer has helped enterprises around the world scale their regulatory data operationalization efforts to do just that.
As part of its impressive growth and expansion, a global telecommunications company needed to roll out a new content delivery network for a high-growth offering: high-speed video player services. Management made an initial investment in network infrastructure, rolling out limited amounts of upgraded bandwidth in strategic geographies.
Big data analytics allow you to evolve your analytics by answering the next level of detail – how, why, when, and where it happened. This enables you to answer a new generation of questions to become an agile business and allows the BI and analytics team to deliver more insights to your organization.
Customer behavior analytics maximizes the value of customer relationships by identifying actionable insights that drive valuable outcomes. Whether to acquire new customers, better engage, or retain existing ones or increase loyalty, customer behavior analytics is the fundamental core to make those initiatives successful.
As big data moves toward greater mainstream adoption, its compliance with long-standing enterprise standards and industry regulations is becoming increasingly important.
Big data can only get bigger. Data lakes help you manage data in all forms, shapes, and sizes. But how do you get more value from all this data?
Regardless of whether the enterprise selects the platform or standalone approach for self-service data prep, maximum compatibility with data sources – both on-premises and in the cloud – should be a key priority in selecting a product. Download this study to learn more.
Financial services institutions are under continuous pressure to identify ways to grow their revenue and assets under management. At the same time, competitive threats abound as financial services firms of all types compete for customers and their wallet share.
With a great deal of personal data being used in big data analytics, you must choose a platform that provides the deepest functionality to ensure you are GDPR compliant while still lowering the administrative burn needed to manage compliance processes. Datameer fits the bill.
Datameer’s big data analytics platform provides the right combination of power, speed, and flexibility required to navigate the unpredictable waves of financial services compliance requirements successfully.
An effective data engineering platform can help retailers harness this vast amount of data to optimize the customer experience, increase sales across all channels, and make merchandising a data-driven process.
Big data analytics combines and analyzes data related to customer interactions, innovation, service execution, and network performance, providing a 360-degree service experience view. This process allows winning CSPs to retain and grow their customer base, increase service utilization, and raise customer lifetime value (LTV).
Data-driven organizations need a modern data pipeline to ensure business users of all stripes have the right data at the right time to make decisions and take expedient actions. This report provides an overview of the modern data pipeline and how to implement it.
Explore How Big Data Analytics Can Transform Operations, Enhance Customer Experiences, and Boost the Bottom Line.
Customer analytics, driven by big data, can transform the buyer-seller relationship. It’s not simply about gaining deeper insights about customers but using that data to drive more effective personalized marketing, increasing sales productivity, and retaining customers for a higher lifetime value.
This report shows how each provider measures up according to two business use cases (native Hadoop BI for strong user interface and strong data preparation) and helps application development and delivery (AD&D) professionals make the right choice.
Hadoop plays an important role in big data architecture in many organizations. But with 60% of big data projects projected to fail, many companies are scrambling to find ways to ensure their big data efforts can succeed. Big data analytics platforms provide a faster path to prove business value and eliminate two risk components that can upend your big data initiatives.
We examined several different approaches and architectures for governance. Choose the right approach for the unique needs of your organization, data, analytics, and business teams. With Datameer, you can mix and match these models to fit individual needs with departments or business units.