STREAMLINING YOUR DATA PROJECT WITH A SISENSE- MONGODB MASHUP 1 INTRODUCTION Some things just go together. Cheese and wine. Starsky & Hutch. Windows Vista and… we’ll get back to you on that one. And now: MongoDB reporting and Sisense BI analytics. Okay, so it’s not the catchiest-sounding duo. But what matters is that, while these two technologies are great on their own, together they create a formidable force that will clear up your data woes, serve the needs of programmers and analysts, and seriously drive your business forward. Let us show you how. 2 What Is This MongoDB You Speak Of? How Does It Connect to Sisense BI Software? Ok, Sure… But Why Would I Want to Do That? MongoDB: The Weak Spots A Match Made in (Data) Heaven How It Works for Our Customers: CAST Final Thoughts: How MongoDB and Sisense Can Contents Streamline Your Business 3 WHAT IS THIS MONGODB YOU SPEAK OF? If your organization already uses Expect a high write load, especially MongoDB, feel free to jump to the next when loading high volumes of data where section. If you’re unsure exactly what it each individual data line has a relatively does, or are thinking of investing in the low business value platform, here’s a quick recap. Need to partition and shard your MongoDB is a powerful, flexible, open- data, allowing you to grow faster than source database that’s built on a NoSQL MySQL comfortably allows architecture of documents and collections Use location-based data, as the rather than rows and tables. In other spatial functions help you find the data words: a document-oriented data model. you need from specific locations quickly Unlike relational databases like data and accurately warehouses, MongoDB centers around documents made up of key-value pairs Are looking for high availability, and stored in collections. even in an unreliable environment. That goes for cloud an on-premises. You can This means that data stored in MongoDB easily create replicaSet servers and isn’t subjected to the same restrictions recover data if a node or data center fails as it would be in a relational database. Instead, it allows for dynamic schema Know your data sets will be huge, design, in which documents in the same and a schema-based system could cause collection can have different fields and a ton of trouble when you add rows and structures. Data is sharded, making the columns later database far more agile and scalable than a huge, slow-moving, space-and-memory- How Does It Fit in with Sisense BI hungry data warehouse would be. Software? Overall, you would use MongoDB With the help of Simba technologies, when you: we’ve created a Sisense-MongoDB connector that lets you tap into any Are creating a networked application MongoDB source via ODBC connectivity. designed to run fast and seamlessly, and That way, you can query and analyze don’t want your data to be the thing that all that unstructured, sprawling data in slows it down your MongoDB database using Sisense’s Business Intelligence tools. 4 Ok, Sounds Great… But Why Would MongoDB: The Weak Spots I Want to Do That? Right, let’s take a look at the biggest analytical challenges that come with Good question. There are three parts to this. working with MongoDB. 1 It Takes the Pressure Off Your IT Team First up, while it’s always nice to have If you’re a MongoDB user, this gives your freedom, unstructured schemas are a non-technical colleagues an easy way nightmare for analytics. If you’re using to query, interpret, and visualize data in MongoDB, you’re going to need an the database without you having to get intelligent, powerful renormalization involved. We’ll talk about this more in a process in place to make all that data moment. consistent, coherent, and measurable before you can do anything useful with it. 2 You Don’t Need to Learn the MongoDB Query Language or API to Do It … Next, while MongoDB lets you extract data And you can run queries in SQL, even from all over the place, it’s less effective at though MongoDB is a NoSQL platform. bringing them all together. Organizations, Plus, you can easily customize all schema large and small, increasingly operate with using Sisense’s schema editor. You don’t multiple systems and data sources, and they even have to configure the system for need to be able to mash all of these together automatic schema generation, as the to get a complete picture of their business. driver does an awesome job of this And then there’s the issue of who needs by itself. Basically, this is a great way to access this data. After all, MongoDB is to take advantage of all that data to very much a technical, developer-friendly generate top business intelligence, using environment. That’s great for you techie a system that’s highly compatible and types, but it’s less compelling for your easy to set up. business-focused colleagues. How do you 3 It Gets Data Ready for Use marry this up with the needs and skillsets While MongoDB is excellent at what it of the people who need the insights? does, it does have a few weak spots when And lastly, there’s the question of scale. it comes to preparing data for effective MongoDB is amazing in that you can analysis. Collaborating with Sisense is the keep adding new data to it forever perfect way to fix that. without spending tons of money on an expensive infrastructure, but you also need to think about scaling up access to it within your organization. That 5 means finding ways to automate as MongoDB Gives You a System Designed much of the analytical process as possible for Programmers. within the business, and then making this available to hundreds, perhaps even Sisense Gives You a System Targeted to thousands, of users. Analysts. Both are invaluable for business A MATCH MADE IN (DATA) HEAVEN intelligence: your programmers need scope to integrate and build on top of the Opposites attract, right? And often, it’s core framework to suit their data needs, the biggest differences that make an and your analysts need to access complex unlikely pair work so well (ehem, think of data sets without deep-level expertise. a salted caramel). Merging MongoDB and Sisense means In the case of MongoDB and Sisense you can support and translate between BI, we’re talking two approaches at both sides of the business – the the opposite ends of the spectrum: technical and the analytic environment. operational data versus analytical data. It’s precisely these differences that make MongoDB Gives You Freedom in How You the two such a powerhouse couple. Record Data. Let us explain. Sisense Gives You a Coherent Map to Explore that Data. MongoDB Gives You Dynamic Schema. MongoDB is supremely flexible. You Sisense Gives You Robust Renormalization. can set many different attributes and you don’t have to define the schema With MongoDB, you have all the benefits upfront, meaning that within any entity, that come with schema-freedom: great document, or collection there are a vast options for content management, number of attributes you may find useful personalization, and any other further down the line. environment that demands a flexible approach to captured information. When it comes to data projects, though, But you also need to reform and you need to find a way to homogenize renormalize that information for analysis. these records in order to cross-reference And that’s exactly what Sisense does. and analyze the data. 6 Because of this, many analytics platforms languages. To automate much of the are too narrow and rigid to be able to process of intelligent renormalization, handle sprawling, unstructured data. But making MongoDB’s complex schemas Sisense’s system is columnar - it copes workable without tearing your hair out extremely well with multiple columns along the way. or fields. In fact, we support this kind of environment out-of-the-box as standard, It also means utilizing Sisense’s with no additional coding required. columnar technology to process information from large and complex One of the reasons that MongoDB is models, creating different views and so popular is that it works fast while analytics out of the data and processing keeping infrastructure costs low, and queries from huge datasets in the blink it does that through horizontal scaling. of an eye. That also means that data sets stored in MongoDB can be enormous. It means creating a system where business users handle their own ETL, Again, Sisense is designed to help you creating reports and dashboards without cut straight to the specific insights you you having to hold their hands. need, but it doesn’t limit you to one area the way that, say, a data mart And it means headache-free access would do. In fact, the columnar structure to SSL and other integrations offered combined with innovative use of in- through MongoDB – as well as memory processing (In-Chip technology) aggregation, buffering, and batching means the system can actually support tools that improve performance and very large and complex data sets for make it easier to export data. analytical purposes. Meanwhile, for analysts, it means Together, MongoDB and Sisense taking control of their own data projects Appeal to Your Whole Team and automating the flow of data from MongoDB for analysis, even when their In short, when you bring together the technical knowledge is limited. different strengths of MongoDB and It means they can use an intuitive front- Sisense, you create something that fits the end system to create custom formulas needs of users right across the business.