5 Killer Reasons to Upgrade to SlamData 3.0
Earlier this week, we released the most significant version of SlamData yet: version 3.0, which has been in development for more than a year and a half!
This version of SlamData features a completely new UI and a bunch of bug fixes, performance improvements, and new SQL functions.
In this post, I’m going to highlight the 5 top reasons you should upgrade to SlamData 3.0 today.
1. Beautiful Dashboarding
By far, the number one complaint about SlamData 2.x was the limitations of the notebook interface. Notebooks are great for ad hoc exploration and refinement of data, but let’s be honest: notebooks look absolutely awful!
They’re geeky and scientific, and not in a good way. What people really want to see is drop-dead gorgeous dashboards. Dashboards that can look however you want them to look, and which can be shared internally or embedded into websites and web applications.
I’m happy to say the number one complaint about SlamData 2.x is now, in fact, the number one reason to upgrade to SlamData 3.0!
SlamData 3.0 lets you create and stack cards on a board, and resize and move them wherever you want. You have the full power of the notebook interface to refine, aggregate, and filter data, but now you can make it look stunningly beautiful, too!
2. More SQL Firepower
When working with semi-structured data in JSON files or databases like MongoDB, it’s very common to work with data that has one shape, but needs to be transformed into another shape.
SlamData bakes in the uber-powerful Quasar analytics engine, which has recently gained a bunch of new functions to help working with string data a lot easier!
These new additions include the following:
- BOOLEAN(<string>). Converts strings to booleans.
- INTEGER(<string>). Converts strings to integers.
- DECIMAL(<string>). Converts strings to decimals.
- NULL(<string>). Converts strings to null values.
- TO_STRING(<prim>). Converts any primitive value to a string.
Of course, that’s not all! The full list of additions will be documented shortly on the Quasar website, but in the meantime, there’s one new goodie just too great to pass up: the Is Defined operator!
The Is Defined operator uses the symbol ??. If you perform an operation on the wrong type, then you’ll get back undefined. If you want to add special-case logic if something has the wrong type, then you can use the ?? operator to do it.
For example, let’s say you want to sum all the fields called total, but in some cases, total is a string, and in other cases, it’s null. With the ?? operator, it’s a cinch: SUM((total + 0) ?? 0).
3. Bug Fixes Galore
Our last 2.x release was SlamData 2.5.7, and it was a very stable release — one that most of our users continue to enjoy to this day.
However, 2.x had a number of bugs, both in the front-end (including challenges saving and loading notebooks, bugs on certain browsers, bugs preventing the choice of certain dimensions in the charts, and so on), and in Quasar.
With SlamData 3.0, we’ve reduced the known bug count to microscopic levels. In addition, this is the first release where we hired external QA to come in and break as much as they possible could — with the result that SlamData 3.0 has less than 5 known issues!
Stop working around bugs in 2.5.7, and upgrade to our most stable, robust, and powerful version yet. SlamData 3.0 is the finely-engineered BMW of your dreams.
4. Easy, Secure, Multi-Tenant Embeddability
As most of our users know, SlamData is 100% open source, but we throw some incredible horsepower into our Advanced edition, which is how we fund development of all the awesome open source goodness.
We launched SlamData Advanced a few months ago, and it brought dead-simple authentication, authorization, and auditing to the open source version.
Now in SlamData 3.0, we’ve added powerful and easy-to-use developer APIs that let you embed analytics, charts, and dashboards like never before into your own applications.
With SlamData 3.0, you can now do all of the following with a simple API:
- Create and delete tokens that have particular permissions for each or your customers.
- Embed interactive dashboards into your MongoDB application that are secured by tokens.
- Implement hierarchical multi-tenancy into your embedded analytics and reports; that is, your customers can have users, who see and share different data and reports.
- Customize the look-and-feel of ANYTHING with your own CSS style sheets.
In addition, SlamData Advanced 3.0 ships with powerful role-based security system that lets you divvy up permissions in an organization based on who has what role.
The authentication system plugs into any OIDC provider, including ActiveDirectory, Google Apps, and many more, or you can use tokens for white-label authentication that seamlessly layers into your own application.
5. 100% Open Source
SlamData 3.0 is still 100% open source, which is the most compelling reason of all to upgrade!
You can hop over to our Github repository, download the source code, and build the project from scratch in less than an hour.
In addition, we have introduced a new option for you to purchase fancy GUI installers (so you don’t have to build the source code), with a version of SlamData that’s been rigorously tested. This option includes basic email support and is only $39 bucks!
That’s right, for less than the cost of a dinner at a restaurant, you can get access to our support engineers, and a brain-dead simple way of installing a battle-tested version of SlamData onto your desktop or server infrastructure.
There you have it, five killer reasons to upgrade to SlamData 3.0.
Latest posts by John De Goes (see all)
- Battle of Open Source Analytics: Spark vs Drill vs Quasar - July 20, 2016
- The Five Money-Saving Tricks MongoDB Doesn’t Want You To Know - July 19, 2016
- 5 Killer Reasons to Upgrade to SlamData 3.0 - July 14, 2016
Native Analytics On MongoDb, Couchbase, MarkLogic and Hadoop.
No mapping. No ETL.
Recent News & Blogs
Boulder, Colo., February 23, 2017 – SlamData Inc., the leading open source analytics company for modern unstructured data today announced that it has raised a $6.7M Series A funding round, led by Shasta Ventures. The investment will drive further development of the firm’s breakthrough analytics solution: a single application for natively exploring, visualizing and embedding analytics against unstructured data sources including NoSQL, Hadoop, and cloud API’s.read more
SlamData just released its first update of 2017, SlamData 4.1.1. It delivers a number of new UI enhancements, performance improvements, new charts, as well as commercial releases for the Couchbase, MarkLogic and Spark/Hadoop connectors.read more
Welcome to the SlamData getting started video. Let’s jump right in. By default, SlamData runs on port 20223. You can change the port it runs on by modifying the quasar-config.json file. By default, this file is located in the following directories on Windows, Mac and Linux.read more
To help shed light on where customers can go to address their data-driven challenges, Database Trends and Applications magazine assembles an annual list of solutions…read more
The following is an interview I conducted with Jeff Carr, CEO and Founder of SlamData regarding the trends in enterprise business intelligence.read more
Enterprise Business Intelligence solutions are failing, and the reasons are very obvious. Leading analyst firms including Gartner and G2 have published rankings which show virtually no leadership and scant challengers in the market.read more
Who Is Using SlamData?
Whitepaper: The Characteristics of NoSQL Analytics Systems
by John De Goes, CTO and Co-Founder of SlamData
Semistructured data, called NoSQL data in this paper, is growing at an unprecedented rate. This growth is fueled, in part, by the proliferation of web and mobile applications, APIs, event-oriented data, sensor data, machine learning, and the Internet of Things, all of which are disproportionately powered by NoSQL technologies and data models.
This paper carves out a single concern, by focusing on the system-level capabilities required to derive maximum analytic value from a generalized model of NoSQL data. This approach leads to eight well-defined, objective characteristics, which collectively form a precise capabilities-based definition of a NoSQL analytics system.
These capabilities are inextricably motivated by use cases, but other considerations are explicitly ignored. They are ignored not because they are unimportant (quite the contrary), but because they are orthogonal to the raw capabilities a system must possess to be capable of deriving analytic value from NoSQL data.
Table of Contents
- The Nature of NoSQL Data
- NoSQL Databases
- Big Data
- A Generic Data Model for NoSQL
- Approaches to NoSQL Analytics
- Coding & ETL
- Real-Time Analytics
- Relational Model Virtualization
- First-Class NoSQL Analytics
- Characteristics of NoSQL Analytics Systems
- Generic Data Model
- Isomorphic Data Model
- Unified Schema/Data
- Polymorphic Queries
- Dynamic Type Discovery & Conversion
- Structural Patterns