Why Your Data Kitchen Needs Separate Stations​

The case for splitting Analytics from Operations

Introduction: How I Learned to Separate My Kitchens​

After 30 years as a Data Architect, I’ve seen the same scenario play out countless times. Companies, particularly those in the SaaS space, come to me because they’re struggling to scale their analytics. They’ve built impressive systems, but as their data needs grow, so do their problems. Often, the root cause is the same, they’re trying to analyze data directly from their operational stores.

Imagine trying to build detailed reports for hundreds of clients, each with their own unique needs and schedules, all while relying on the same operational system that’s supposed to keep your business running smoothly. It’s a recipe for disaster. Whether they’re using a relational SQL store or a memory-hungry NoSQL solution, these companies are burning resources and slowing down operations.

What’s more, the market often pushes the idea that copying data is inherently bad and that accessing data directly from wherever it resides is faster and more efficient. But from my experience, this approach can lead to bloated systems, sluggish performance, and skyrocketing costs. The truth is, copying data isn’t bad, as long as it’s done thoughtfully, with a focus on directionality, master versus reference data, and lifecycle management.

An Analogy:​

Imagine you're running a restaurant kitchen. You have chefs preparing food for hungry customers, and every second counts. But what if, in the middle of dinner service, a food critic walks in and asks for detailed recipes, nutritional information, and a history of every dish you've ever served?

If the same chefs handling customer orders had to stop and dig through old recipe books, calculate nutrition facts, and compile a history of dishes, the kitchen would grind to a halt. Orders would get delayed, customers would be unhappy, and the overall dining experience would suffer.

This is what happens when you don't separate analytics from your operational data store.

The Operational Data Store (ODS): The Cooking Station In our kitchen analogy, the Operational Data Store (ODS) is like the cooking station. It's where all the action happens, data is created, updated, and used in real-time, just like how chefs are constantly chopping, sautéing, and plating dishes for customers. The ODS is optimized for speed and efficiency, designed to handle high volumes of transactions quickly and reliably.

Analytics: The Food Critic's Desk Analytics, on the other hand, is like the food critic's desk. It's where deeper insights are drawn, trends are analyzed, and strategic decisions are made. This process requires access to large amounts of historical data, complex calculations, and the ability to look at the data from various angles, similar to how a critic might assess a dish from taste, presentation, and nutritional value perspectives.

Why They Should Be Separate​

If you were to try and handle both cooking and food critics' demands from the same station, you'd overwhelm your chefs, slow down service, and ultimately hurt your restaurant’s performance. The same thing happens in your data architecture if you try to process analytics directly from your ODS:

Performance Hit: Just like chefs can’t keep up with orders if they’re constantly interrupted, your operational systems slow down when bogged down by complex queries.

Data Contamination: Mixing operational and analytical processes can lead to inconsistencies in data. It’s like a chef accidentally mixing salt instead of sugar, small errors can have big impacts.

Scalability Issues: As your restaurant (or business) grows, the demands on both your ODS and analytics will increase. Keeping them separate allows each to scale according to its own needs.

The Solution: A Separate Analytics Kitchen​

In a well-run restaurant, there’s a separate space where food critics are entertained. They have access to all the data they need, but they don’t interfere with the daily operations. Similarly, in data architecture, we separate the analytics environment from the operational data store.

Data Warehouses: These are specialized kitchens designed for analytics. They store historical data in a specific way to support complex queries, and are optimized for analysis without affecting operational performance.

ETL Processes and Data Pipelines: Just like how the best ingredients are carefully selected and prepared before they reach the kitchen, data is extracted, transformed, and loaded (ETL) from the ODS into the data warehouse. This ensures that the data used for analysis is clean, consistent, and ready for deep dives.

Conclusion: Keep Your Kitchens Separate!​

By keeping your operational and analytical processes separate, you ensure that both run smoothly, customers get their food on time, and critics get the detailed information they need without disrupting the flow. In the world of data architecture, this means faster operations, more accurate insights, and a more scalable and resilient system.

And on that note... I think I'm in the mood to cook. Maybe some shrimp in a roasted red-pepper and white-wine cream sauce on delicious home made pasta... I like quality in my cooking, almost as much as I like it in my data!

About TrillaBit​

TrillaBit is an analytics and business intelligence (BI) software company founded in 2022 and headquartered in Toronto, Canada. The company provides a no-code, search-driven, low-cost analytics cloud platform tailored for B2B SaaS providers. TrillaBit's platform, Quick Intelligence, delivers fast, easy, and secure access to data, allowing product owners and business users to create dashboards and visualizations without developer assistance.

The TrillaBit platform is a hosted, highly dynamic, meta-data engine that points to client data stores and automatically generates queries based on configuring tokens in search (AKA Search-driven analytics). The platform creates smart visualizations then allows users to refine the results. This ease of use and exploration allows end users to quickly dive into the data and create their own dashboards to derive further insights instantly. All without the need to rely on developers.

Quick Intelligence is designed to handle complex security scenarios, including multi-tenant environments, and supports a wide range of data sources. TrillaBit emphasizes data monetization through visualization, making data easily accessible and actionable for its users. The platform is scalable and affordable, designed to meet the needs of both small and large enterprises. For more information about TrillaBit, please visit: www.TrillaBit.com or [email protected]

Tracking Over 1 Billion Public Websites for Real-Time AI Insights​

TrillaBit and Accern partner to provide a comprehensive tool for those seeking to gain a competitive advantage during this election cycle. Whether it’s for developing AI models, executing timely trades, or crafting informed narratives, the visualization of Accern’s 2024 US Presidential Elections dataset, including candidate, event, and topic sentiments, serves as a critical resource for a diverse range of users

NEW YORK, NY / ACCESSWIRE / July 4, 2024 / TrillaBit is excited to announce their partnership with Accern, and the launch of a new product to deliver 2024 US Presidential Elections insights in an innovative and powerful way.

TrillaBit and Accern’s partnership is truly complimentary. TrillaBit re-imagines traditional means of delivering analytics with additional features to support B2B SaaS companies in delivering value and understanding their customers. Trillabit’s platform benefits B2B SaaS companies and their customers providing ease of data exploration and creation of KPIs and dashboards without having to depend on developers.

Accern has developed a powerful NLP processing system capable of mining billions of sources for specific signals, events and topics in real-time. A targeted system to obtain unique sentiment and volume information not previously available. Until now Accern has delivered this unique data via APIs and close relationships with their customers.

TrillaBit and Accern’s partnership brings a whole new level of insights to the public. Combining Accern’s uniquely powerful datasets with TrillaBit’s flexibility and easy-of-use has opened the doors for the general public to subscribe and explore this data in their own way. Default dashboards are provided, but subscribers are also able to create their own analytics and dashboards to derive their own unique insights in real time.

Accern’s unique new dataset for the 2024 U.S. Presidential Election is designed to provide unparalleled insights into presidential candidates, their campaigns, policy implications, polling data, and public appearances. It is updated daily and delivered in real time, with historic analysis and flexible exploration capabilities.

TrillaBit has extended its product family and is now able to create online storefronts for partners to monetize their data and reach a broader audience. This new capability has allowed Trillabit to quickly integrate with the AccernLens store as well as Stripe for subscription management and payment processing.

See here: accernlens.trillabit.com

And here: www.accern.com/lens?lens=Government

The newly unveiled dataset is powered by Accern's robust NLP models and provides access to over 1 billion public news websites and blogs in real-time. This extensive coverage allows users to track and analyze sentiments surrounding the election effortlessly, and also to look back historically to identify trends.

About TrillaBit​

TrillaBit is an analytics and business intelligence (BI) software company founded in 2022 and headquartered in Toronto, Canada. The company provides a no-code, search-driven, low-cost analytics cloud platform tailored for B2B SaaS providers. TrillaBit's platform, Quick Intelligence, delivers fast, easy, and secure access to data, allowing product owners and business users to create dashboards and visualizations without developer assistance.

The TrillaBit platform is a hosted, highly dynamic, meta-data engine that points to client data stores and automatically generates queries based on configuring tokens in search (AKA Search-driven analytics). The platform creates smart visualizations then allows users to refine the results. This ease of use and exploration allows end users to quickly dive into the data and create their own dashboards to derive further insights instantly. All without the need to rely on developers.

Quick Intelligence is designed to handle complex security scenarios, including multi-tenant environments, and supports a wide range of data sources. TrillaBit emphasizes data monetization through visualization, making data easily accessible and actionable for its users. The platform is scalable and affordable, designed to meet the needs of both small and large enterprises. For more information about TrillaBit, please visit: www.TrillaBit.com or [email protected]

About Accern​

Accern is the leading NLP company empowering enterprises to develop industry-specific solutions. Offering a comprehensive NLP platform, models, data, and chat tailored for multiple industries, the company accelerates time-to-value for leading organizations across Financial Services, Government, and beyond. With a streamlined no-code workflow complemented by pre-built taxonomies, over 50,000 classification models, and access to billions of rows of public data, Accern is revolutionizing operational efficiency across the global workforce.

Accern's remarkable achievements have earned it a notable position in Gartner®'s 2023 Hype Cycle™ for Data Science, Machine Learning, and Emerging Technologies in Banking, as well as recognition by Fast Company as one of the Next Big Things in Tech. Trusted by elite data teams from global leaders like Capgemini, UniCredit, Interactive Brokers, Mizuho Bank, and Standard Bank, Accern facilitates the building and deployment of NLP solutions at scale.

Headquartered in New York, NY, Accern is honored to be recognized as a Forbes 30 Under 30 company. The company has raised $40M from esteemed institutional investors including Fusion Fund, Tribe Capital, Shasta Ventures, Allianz Strategic Ventures, Mighty Capital, and many others. For more information about Accern, please visit www.accern.com.

TrillaBit Dashboard Feature Highlights​

The 2024 Presidential Election Dataset Features​

Use Cases of the 2024 Election Dataset​

The dataset is available now and can be accessed through the Accern Lens Store via an API feed or a Dashboard, powered by TrillaBit. For more information, visit Accern's website.

contact us!

WE’RE RUNNING ON SNOWFLAKE!

How it was​

So we’ve been doing this for a while. Decades actually. Early on I was running denormalized dimensional modeling on row based databases and squeezing every ounce of performance out if it that I could. Pre-aggregating years of transactional data and preprocessing as much as I could to get that fast end user experience. Of course this wasn’t highly efficient or cost effective, and we couldn’t easily or dynamically get back down to fine grained data.

Back then we poured over every detail down to the actual hardware, io and memory etc. (it mattered what the disk controller was and how our raid array was configured), we kept processing as close to the data as possible… basically because we had to, to get any kind of real performance. Then a world of change happened. Distributed processing and columnar stores. Eventually columnar stores became the de facto standard for analytics. This makes a lot of sense. It’s more aligned with how data is read for analytics, reduces io with higher data compression rates, and the models lend themselves better to distributed processing.

Then came big data - and with it columnar based file formats from hadoop, like parquet and orc. The cloud became a bigger thing and data lakes were the way to go. But they weren’t something that was prepackaged for you like the databases of yore. You had to build them almost from scratch, and it wasn’t easy. Your query engine was separate from your index store and separate from your data storage. You needed to handle the writing with integrity on failure. Understanding Hadoop was a big thing and tools felt like they were lego bricks you needed to click together in just the right way.

A better way​

With the arrival of Snowflake things changed for the better again. It handled so much for you, providing that ‘database engine feel’ on big data infrastructure. Beautiful! Because of this, Snowflake became popular in no time. It grew like crazy and became a desired tech because it made modern approaches more accessible.

What was making big data costly and expensive was resourcing (human, hardware), Snowflake helped cut those by abstracting all the complexity of big data ecosystems from developers and letting you just write basic SQL. It handled the hardware aspects of the lake, instead of spinning up and managing a farm of hardware and machines to process everything. Snowflake just took care of it all. The separation of storage and compute allowed you to minimize your data footprint, and maximize your processing… elasticity! Running the required resources for a particular process for a limited period of time lowered your cost (the headache of dealing with node failure was gone too). Your developers could focus on implementing business needs rather than constantly maintaining and enhancing the big data cluster.

Our Value Add​

Enter onto the scene the next step in capabilities and simplification - TrillaBit. We created a smart low-code analytics layer that dynamically runs on top of highly performant and efficient analytic processing platforms like Snowflake. We help you leverage your Snowflake investment further by letting users drill into and explore data at their whim, without knowing sql or other underlying tech.

TrillaBit can now simply point to an instance or multiple instances of Snowflake, locally or globally to provide self service analytics to users in the most efficient and cost effective way.

Thanks,

Keith

contact us!

You can’t swing a bat without hitting something AI these days.

TrillaBit has also stepped into this realm, and we’re genuinely excited about it. Our aim is to provide you with a solid foundation you can rely on.

We get it, cutting through the noise to see what’s really going on can be tough. Somewhere between the lofty predictions of the future and the more tangible implementations like chatGPT (along with numerous other LLMs and RAG pipelines), there is solid ground to stand on.

AI encompasses a wide range of algorithms, techniques, and applications. When you mix this already complex tech world with people’s imaginations, well… understanding the current state can be a bit fuzzy. Often, what you see is a prototype representing just 20% of a vision, followed by a lot of talk about the vision.

I asked chatGPT why the term ‘AI’ is confusing, and here’s the gist of the response:

"Overall, the term 'AI' can be confusing because it represents a diverse and evolving field with implications that extend across technology, ethics, society, and more. Clarity often comes from breaking down AI into its components, understanding its current capabilities, and discussing its potential impacts in specific contexts." ~ChatGPT

Given the confusion around the current state, let’s start by clearing some of this up. Describing the current state is tricky because things are evolving rapidly. It’s a bit like driving a car—you don’t look out the side windows to figure out where you are. When you're going fast, you focus on the road ahead to see where you're headed and those signs just up ahead to know where you are. You might not see the entire path, but you have a good sense of where you're headed.

GenAI Today​

A scope limited perspective

If you ask ChatGPT what applications AI covers today, it will provide a long list and then tell you, these are just a ‘few things’. Obviously the impact on our lives is quite extensive.

What I’m seeing in our space today however still has its limitations. For instance, LLMs are amazing at natural language processing. They have been trained across vast amounts of text. But even so, we’ve still had to augment models with search engine, vector database technology to now include proprietary data within enterprises. RAG pipelines are one way to build a model from some static data and enhance it with other text on implementation, typically proprietary data.

Then we have NLP(Natural Language Processing) for SQL. It’s more task-specific than the general-purpose LLMs like GPT. The primary focus of NLP for SQL systems is to understand and convert natural language queries into SQL, making it a specialized subset within the broader field of natural language processing. But NLP for SQL still isn’t able to create highly complex SQL statements yet, and often still makes mistakes. There’s still a lot of work to do here… If you’ve seen even a portion of the data models I have in my career, you’ll know the craziness that people have implemented. From the strangest naming conventions, to the ugliest of models…. Let’s just say 3rd normal form and above have certainly not been respected almost anywhere. Every implementation is different and learning from this certainly isn’t generic. Although I don’t doubt that AI will be able to navigate this someday, I think we’re a ways off. Today, it’s one model at a time.

Databases are here to stay, and so is the need for data security. We not only have to deal with proprietary data models and information, but also the challenge of securing this data across different roles, locations, and organizational levels.

I believe this is why, for the past year or so, AI in the analytic tooling space has been tied to either tools for developers themselves, or they’ve been applied to a limited context, like spreadsheets. If you have access to a spreadsheet, then you have access to all the data within that spreadsheet, and therefore we don’t need to worry about security when using nlp to dynamically build some dashboard on the spreadsheet.

For developer tools, it’s an efficient assistant. Let AI build out the code quickly, then the developer can enhance it (fix it) and implement it without the security risk.

Currently the best implementations today involve a human in the middle or simplified context. Nothing wrong with this, but why does it need to be a developer in the middle?

Bucking the Trend​

TrillaBit is aiming to buck the current trend. Our vision isn’t to build tools for developers, but build out self-service to ALL users while accommodating the AI inconsistencies that exist today. Practical implementations that users find useful and can work with.

Let me explain... The bandwagon trend for many tools today is to implement whatever exists in AI so they can give it to developers. Let’s say NLP for SQL. As a developer you need to build out dashboards, so you use an AI plugin to ask a question and it will return a SQL statement for you. Now you can use that SQL statement to build out your visualization. Although you may need to tweak it, because it didn’t really translate your ambiguous language well enough and it can’t really create that more complex query that you actually wanted, but hey! you saved a little time, and the product gets to say they’ve implemented AI! Win win!... sort of.

Bucking the Trend: What if… instead, AI didn’t return SQL? What if instead it returned something more simple. Like tags, tags that are not ambiguous and relate to more complex sql statements. Something that is easy to understand, and modify to get exactly what you want!

Imagine asking a question and getting back a graph visualization, and a set of tags that you can easily modify because they’re not as complex as the SQL language. All data returned is secured to you and you can now drill down into that visualization to explore your data.

The SQL is still generated in the backend and securely executed against your data model, but presented in an easy to use way. A more user-friendly way.

TrillaBit GenAI Features​

When it comes to new industry and life changing tech like GenAI, we need to push the envelope and reach for the real value. Copying the next guy might give companies something to talk about, but TrillaBit strives to deliver value. To deliver practical GenAI features directly to the end user.

TrillaBit is already built to leverage metadata over traditional development tools. This approach allows for dynamic control over data exploration, analytics and collaboration with embedded security. Enhancing this dynamic platform with GenAI just makes sense and is the obvious next step in our evolution.

I’ll just highlight a few of the features here, as what we’re actually doing would take so much more time to get into. And we will, so stay tuned!

Early TrillaBit GenAI Features:

In summary I believe TrillaBit is making great strides in innovation and providing a platform to benefit B2B SaaS companies as they strive for competitive advantage in this newly fast moving GenAI world.

We will expand on this as we go, but if you would like to know more, sooner, Please feel free to reach out to us! We’ll be happy to get into more details!

Thanks,

Keith

TrillaBit is enabling Analytic Cards for dashboards.

Analytic Cards​

Analytic cards are used by leading SAAS platforms. Essentially they are a combination of metrics displayed in a way that’s easy for end users to quickly understand, and in some cases further explore their data within some context.

These usually only provide fixed functionality and don't allow end users to create their own versions. Products like Shopify have prebuilt these within their own context and what users can modify is limited to predefined configurations.

Trillabit has figured out a way for end-users to easily create and publish groups of analytics for other users as self-service.

We’re not just building out flat cards like many products have done. We've given our cards all the same capabilities and drilldown to raw data that you find throughout our product. Analytics shouldn’t stop at the first result, you’re going to want to dig and see what’s really going on. What makes up that result and where the most interesting insights are coming from.

A distorted mockup for ip purposes

For B2B SaaS providers, this capability enables Product Owners to quickly produce meaningful and user friendly dashboards for their clients on the fly or for their “out-of-the-box” analytic solutions.

But we’re not stopping at one type of card. TrillaBit is enabling a library of cards to choose from so you or your clients can quickly and easily build out your ultimate dashboards and cards without the need of costly development pipelines.

Why?​

Let us explain a little around why we feel our general approach is important.

TrillaBit’s vision is to make data easily accessible to everyone.

Strategic Necessity​

Implementing analytics in B2B SaaS products is crucial for empowering end users with actionable insights and improved decision-making. Not only does this enhance user engagement through interactive dashboards and customizable reports, but it also enables product improvement through feedback loops and understanding user behavior.

The competitive advantage gained from offering advanced analytics, coupled with revenue growth opportunities and improved customer retention, makes it a strategic necessity. Moreover, timely access to data is essential, ensuring that users can make quick, informed decisions and react promptly to market changes, ultimately driving success for both the end users and the SaaS company.

Timely Answers​

Timely access to answers directly impacts B2B SaaS end users' ability to make informed decisions and stay competitive. In rapidly evolving business environments, quick access to data insights enables users to seize opportunities, mitigate risks, and adapt strategies swiftly. Timely answers allow users to respond quickly to changing circumstances, whether it's identifying market trends, understanding customers, or optimizing operational efficiency. This agility fosters innovation, improves productivity, and ultimately drives success for their businesses. Moreover, in today's fast-paced world, where every moment counts, the ability to access timely answers ensures that users can make the most of their valuable time and resources, leading to better outcomes and a stronger competitive edge.

Benefits to B2B SaaS​

Providing analytics in B2B SaaS products not only benefits end users but also offers substantial advantages for the SaaS companies themselves. By demonstrating return on investment (ROI) and showcasing the measurable impact of their product through analytics, SaaS companies can build trust, credibility, and long-term relationships with clients. These analytics demonstrate the tangible value that the SaaS product brings, illustrating cost savings, revenue increases, efficiency improvements, and other key performance metrics. Through clear ROI metrics and reports, clients can see the direct correlation between using the SaaS product and achieving their business goals, leading to increased customer satisfaction, loyalty, and potentially upsell opportunities. Additionally, by understanding how clients are using the product through analytics, SaaS companies can further tailor their offerings, optimize features, and develop targeted solutions, ensuring on-going delivery of value. Ultimately, providing analytics for ROI not only strengthens the SaaS company's position in the market but also solidifies its role as a trusted partner in its clients' growth and success.

Guidance from Power Users and SMEs​

In B2B SaaS companies and specifically the power users within the organization, there exists a deep understanding of the businesses intricacies and needs. These individuals take on the pivotal role of preparing data in a manner that is easily consumable and actionable for their colleagues or clients, who may not possess the technical acumen to create them themselves. This involves translating complex datasets into user-friendly dashboards, selecting pertinent key performance indicators (KPIs) that align with specific objectives, and crafting visualizations that resonate with the business goals at hand. These power users are instrumental in delivering templated data visualizations that empower their colleagues or clients to explore and make informed decisions without the need for extensive data manipulation or analysis. This strategic approach ensures that end users, with their own grasp of business context, can effortlessly consume and derive thier own personal insights from the configured visualization or card. Ultimately, this collaborative effort and culture of data-driven decision-making enables entire organizations to leverage data effectively and achieve their desired outcomes.

Revolutionizing Dynamic Dashboard Creation​

With a self-service tool such as TrillaBit Quick Intelligence combined with Analytic Cards to provide guidance, we are revolutionizing the dashboard creation process. Power users can use these cards as templates for individual KPIs or groups of KPIs within dashboards - swiftly and effortlessly assembling impactful, intuitive data visualizations. By following the layout and design principles of the proven products like Shopify and others, Analytic Cards guide users through selecting relevant comparative metrics, choosing suitable chart types, and arranging visualizations for maximum clarity and impact. With features like guided drilldown, color palettes, and layout options, power users can easily customize the cards to suit their specific needs. This approach not only streamlines the dashboard creation process but also ensures that even users with limited design or technical skills can produce polished and insightful presentations. The results give power users and non-technical business analysts the ability to quickly generate compelling, user-friendly dashboards for clear communication of insights and informed decision-making within their organizations.

We will keep you posted on further developments of Analytic Cards other exiting upcoming features. Feel free to reach out to us at [email protected].

Thanks,

Keith

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We are very excited about the year ahead as we bring out more advanced capabilities, manage strong growth, and support our existing customers.

It's a new year and a new look. We've been busy improving our product with better UI, geocoding support, and more.

Cheers!

The Trillabit Team

ß TrillaBit Quick Intelligence is a robust SaaS platform for reporting and business intelligence, utilizing the power of ClickHouse for fast scalable results. Today's reporting tools are simply not dynamic enough for users to ask new questions and get results back immediately. Not without having to go through a timely and costly development life-cycle.

They also commonly depend on expensive expertise to implement, maintain, and run the supporting systems. When development teams want to spend time on new and exciting creations, they're often pulled back into a business user's new question. They are then forced to build out the new query and KPI, QA it, deploy it, so that the business user can finally see it. Once they see the results they have even more questions which keeps this vicious and costly cycle going.

Working with ClickHouse​

ClickHouse wasn't TrillaBit's first love. Solr originally caught TrillaBit's eye. Why not?! TrillaBit is a search-driven analytics platform, so why not use a search-driven data backend. Solr is capable of some levels of data aggregation, the models are dynamic and the indexing is ideal for search purposes. However, TrillaBit soon ran into a number of challenges. Solr, being a key-value store is more suited to search than it is to high volume aggregation or data compression for performance. Its query language isn’t as broad or established as SQL. It doesn’t handle joins well and is not ideal for managing data. TrillaBit experienced far too much pain managing and getting Solr to perform at scale. So when TrillaBit’s eyes began to wander, ClickHouse showed the most potential as an alternative.
TrillaBit quickly found a new favourite. ClickHouse has a huge number of built-in functions, supports data clustering and is built for both data management and analytics. It handles joins and materialized views with ease. The different table engines [ReplacingMergeTree, AggregatingMergeTree, MergeTree, S3 table engine] all help with different data management use cases for different client needs. The community version is Free and helped TrillaBit get started at a minimal cost As TrillaBit grows, ClickHouse is able to keep pace with the ClickHouse Cloud. Helping even data experts like TrillaBit scale and manage their clusters.

Exploring your ClickHouse data with Quick Intelligence​

TrillaBit is solving the BI Assembly line problem in a cost-effective way. The Quick Intelligence platform allows users to ask a question in a search bar and get immediate visual answers. Example Question: Total Sales by Sales Rep Last Month Utilizing ClickHouse because of its incredible performance at scale, it finds the data and instantly graphs it for you. Once you visualize the data you can easily drill down into the area of interest to uncover further insights and expose record level detail at any point. A metadata driven system allows business users to explore data in their own way, asking new questions and getting immediate answers in seconds.

Save and Share​

When users find something interesting and valuable in their data, they often want to save and share with others, either inside or outside the tool. There are many ways to do this. Creating dashboards on the fly and sharing them with individuals or groups is one way. With Quick Intelligence, this is as simple as pinning visualizations to a dashboard or creating a new one in seconds.

Users can also export their KPIs as images for PowerPoint presentations, word or email. You can also drill right down to the underlying raw data and export it to Excel to share with a colleague.

To Embed or not to Embed​

Companies that want to use this functionality as their own have the option to embed Quick Intelligence into their own product. They can skin it to look like their own brand or to look like any of their client’s brands at the account level. Other companies who want to use this internally are able to have all of this functionality in a standalone UI. Additionally Standalone and embedded are available in a single implementation. For the best of both worlds.

Security and scale​

TrillaBit Quick Intelligence utilizes ABAC policy control. It allows for multi-tenant within multi-tenant capabilities and can secure data for many departments. A large part of the backend scalability comes from the efficient performance of ClickHouse. Whether it's YOUR ClickHouse environment, the ClickHouse cloud or have TrillaBit manage everything, the product is versatile and able to handle several configurations. TrillaBit scales to IoT and network level traffic speed and size of data, trillions of rows. Providing real time analytics.

Getting Started with TrillaBit on ClickHouse​

TrillaBit is an enterprise grade platform. If you have ClickHouse already, TrillaBit can connect to it and you’ll be up and running in no time! TrillaBit is metadata driven, so the only thing required is the data. If you’re looking to run your own data warehouse in ClickHouse and have TrillaBit run on that, just let TrillaBit know. They’ll work with ClickHouse and guide you through the whole process. If you want to be completely hands-off, TrillaBit can handle the end-to-end process for you. Your business users or clients will be able to just start exploring on their own and gathering insights. sPlease feel free to reach out: [email protected]