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A data warehouse solution for credit unions and banks who want to hear more “yes” and less “no"

Join the financial institutions light years ahead of the pack.

Gemineye partners with the brightest banks and credit unions across the country.

$16.7B Assets

Tampa, FL

$645M Assets

Danville, PA

$2.2B Assets

Lewiston, ID

$1.1B Assets

Purchase, NY

$1.3B Assets

Oceanside, CA

$1.1B Assets

Traverse City, MI

Transform your data analytics experience from impossible to empowered.

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Our extendable platform allows for a customizable data warehouse to fit your specific needs now…and as you grow.

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Our implementations often take less than 8 weeks to be fully operational, a sliver of the time compared to others.

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Our flexible infrastructure plays well with virtually every integration, even the ones that are notoriously tricky.

Built for modern financial institutions

Whether you are a $200M credit union or a $10B bank, every organization should have access to a data solution that works the way they need it to. Our personalized approach to each engagement ensures that your specific needs and goals are captured for maximum results and ROI. We leverage modern, break-through tools to provide credit unions and banks with customization without the hefty price tag or lengthy timeline.

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Hear from Our Clients

We selected Gemineye as our data partner due to their remarkable flexibility, competitive pricing, and unparalleled personal touch.  Their commitment to tailored assistance made them the clear choice for our needs.  Since our launch with Gemineye, our data analytics capabilities have allowed us insights into our business that we never had before.

Mike Thomas – Homepage
Mike Thomas
Service 1st FCU
$645M Assets
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The Gemineye Data Lakehouse, built for you

The Gemineye Lakehouse is a single, cloud-native platform that leverages the best elements of a data warehouse and a datalake, saving you time and money in big ways.

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Gemineye Data Lakehouse entered apps channel screenshot
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For those sick of being a sardine

Break free of the tiny, dark can with a data analytics partner who adapts to your specific needs. Customization and dedication guide this ship. Hop in.

Let's start at the beginning

Laying a solid foundation is key to a succesful, long-term data analytics program. Instead of rushing through critical details and complex issues, we believe that the best data analytics program starts with a:

– personalized, concrete strategy

– clearly defined roadmap

– aggressive implementation plan

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The most flexible solution available to banks and credit unions


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News and Resources

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What is a Data Warehouse?

If you’re old enough to remember the days of computer punch cards, floppy discs, or even CD-Roms, you know that data takes up space! Today we’ve gotten more efficient, and we’re able to fit massive amounts of data into devices with microchips no larger than our fingernails. Yet when it comes to digital storage and data that lives online, we rarely give a second thought to where that data actually sits. If anything, most of us have a vague notion of ‘the cloud.’ We know that so much of our information today lives in ‘the cloud’ – this invisible entity that we picture floating all around us, but rarely do we give a second thought to what the cloud actually is, or how it works. We just presume that our data is stored safely somewhere until we get a warning from some watchdog telling us otherwise. In short, you’re….partially right. That data actually does live somewhere, even if that somewhere is within the cloud. Enter a data warehouse. So What is a Data Warehouse? A data warehouse today is basically the cloud-based version of a warehouse where a huge amount of data can be stored. It can be a warehouse for all of your historical data, from past reports, transactions and point-of-sale systems, customer forms, and other databases. It effectively consolidates all of this data, so that it’s easily categorized within one system, and easily searchable. Historically these data warehouses were hosted on-site on large mainframe computers, but today most live in the cloud. Much like a brick and mortar warehouse, these data warehouses usually contain several tools that help users navigate the data, such as comparing different data sets for analysis, as well as systems in place to help with categorizing, analysis, and reporting. Data warehouses are crucial when it comes to analytics and reporting. In order to truly understand the value of your financial services organization, you can’t just look at data taken from that day, that week, or even that same year. You need a historical picture that understands reporting over a lengthier period of time, and that’s where data warehouses can truly shine. Why Data Warehouses are Important If you’re doing any sort of business intelligence and analytics, you’re likely making use of a data warehouse. You may have access to the raw data from different sources and in different places, but putting them together into a format that gives you any insight is going to take significant time and resources. Instead, a data warehouse lets you extract the data, transform it ahead of time into more usable formats, and load it into the warehouse. Having a data warehouse is just one step of your greater data strategy. This can ensure that not only is your data consolidated in one place, but it’s done in a way that makes your data efficient, easily accessible, and a critical tool for your business analytics.

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What is a Data Lakehouse? Hint: It’s Got Nothing to do With Keanu Reeves

Picture it: Keanu Reeves and Sandra Bullock play an architect and a doctor who live 2 years apart in the same property and exchange letters over time…   Wait, that’s just the 2006 movie The Lake House! That has nothing to do with data!   We’ve already spoken about data warehouses, but they’re not the only method for storing data in large volumes. You might have heard about data lakehouses, and while you too may have gotten confused with the film, we’re sorry to tell you that a data lakehouse isn’t a beautiful waterfront property filled with millions of pieces of data (that’s also free for rent on weekends).   So What is a Data Lakehouse?   In short, data warehouses are structures that are designed to store data in a tailored, categorized format, like how we store most of our personal computing data in files and folders. A data lake, on the other hand, stores all of that data in its raw format – it takes all of your data from all of the sources provided, and saves it as is, uncut and uncensored.   This means that data lakes can store data that’s been manipulated, like tables and spreadsheets, right alongside the raw input data itself. The reality, though, is that this data is critically important but it can also be messy. It’s valuable, but can be incredibly complicated to organize out of the gate before anyone even dives in to find what they need.   Why are Data Lakehouses Important?   Data lakehouses essentially combine the concepts of a data warehouse and a data lake together. They allow for the storage of all of that data in a raw format, but use tools like a warehouse to make that data easy for business intelligence, reports, data science and analytics, and machine learning.   Instead of transforming the data before sorting it, data lakehouses use a metadata layer to track files and understand what they are, such as multiple variations of a single table. These allow end users to have better access to the data for research purposes without the labor of categorizing the data in advance.   This allows you to use the raw data in a data like much like you would in a data warehouse. It makes the data easily accessible for analytics, but data lakehouses are an even better fit for those looking to pursue AI. In the financial services world for example, AI can build in the necessary privacy framework to overlay your raw data, and then transform it into something usable for analysis and research.   For your client data, you’re often dealing with significant data besides financials such as recordings of customer service phone calls, CCTV video from branches, etc. A data lakehouse is the place to store it in its raw format, but then allow a privacy framework to protect the data so that only the permissible parts are usable by AI to garner better business intelligence.

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What is a Data Lake?

If you’ve ever tried swimming in a data lake, you know what disappointment feels like.   Not only is there no water (it’s bad for the computers), but those sharp things byting (see what we did there??) at your ankles are probably spreadsheets, graphs, and emails that you tried to ignore while you were still back on dry land.    So, the first big lesson is: don’t go swimming in a data lake.   But what is a data lake anyway…and why are they so incredibly dry?   We’ve written about data warehouses, which are giant repositories of formatted data that are categorized and sorted for easy access. Data from different teams, departments, or any other subset can be grouped together so that it’s noted to belong to that group. The data is neat and clean, just like a well-organized warehouse.   Yet when you have massive amounts of data coming at you all at once, sometimes that processing and organization is just too laborious. You may intentionally want to keep your data in its raw form, so that you’re not wasting any time manipulating it or cleaning it up. This includes everything from emails to spreadsheets to images to PDF files.   In order to store all of your organization’s data in its natural state, you’ll need what is known as a data lake.    A data lake is called that because it’s home to just about everything in its purest, raw form. This can be data from spreadsheets and databases, to tables and websites, images, audio, and even video. Using a data lake means that it can all live together in the same place, and while it can be curated from there, it does not need to be to enter the lake.    The benefit of having a data lake is that you can access what you need, when you need it, without the unnecessary time or expense of grouping and manipulating data that you’re not using. For example, if you were looking to access financial data from the organization for multiple years, or listen to customer service calls from a specific period, you could access both easily from the same central repository.    Depending on the size and needs of your financial organization, you may have a data lake as just one of your many data storage tools. You may also be utilizing a data warehouse, which stores your data in a crisp and organized format as opposed to the massive pool in a data lake. You may even be utilizing a data lakehouse, which is a combination of the two that stores that raw data, but overlays formatting to make it more readable.

News and Resources

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Credit Union and Bank Data Governance: A Primer

In the financial world, your life is all about numbers.   In the world of 21st century finance, it’s really all about data.   Data was always key to the …

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Why Does Good Data Management Matter to Your Members?

When running a financial institution you know that data is an important part of your operations. You’re regularly collecting customer (for our bank readers) and member (for our credit union …

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Set a Course: Tracking (and Correcting) Your Data Analytics Progress in 2024

Hear this: Jack Henry’s 2023 Strategic Priorities Benchmark Study found that 42% of credit unions ranked leveraging data for strategic insights as their number one strategic priority. As more small …

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Ready to finally have control over your data analytics experience?

We offer complimentary consultations – never pushy, always honest.