Author Archives: Spencer Uresk

2017 Tax Legislation Calculator

With both the Senate and the House proposing substantial tax changes in the last few months of 2017, there has been a lot of discussion around the various plans and taxes in general. It would be almost impossible to craft tax legislation without some groups of people coming out better than other groups, and the current crop of legislation is no exception.

Unfortunately, these tax changes – and taxes in general – are very difficult to fully understand, and lots of people have been discussing these changes using information that is incomplete and/or incorrect. For example, it is easy to look at the reduction in tax rates but miss the fact that the personal exemption is gone. I’ve also seen lots of people using tax brackets incorrectly in popular Twitter posts.

I have my own personal views on these proposed pieces of legislation, but what I think about them isn’t very interesting. My goal, rather, is to help better inform the debate around them, because I think it is important to have these discussions with information that is as full and accurate as possible.

I’ve been working on a bunch of content that I’ll be releasing over the coming week, including blog posts explaining various parts of the tax system, some interactive calculators, and more. Tonight, I’m releasing an early version of my first interactive calculator that you can use to see how the proposed changes to the tax code would affect you. You can view it here.

This calculator is far from complete (I’m just one guy and there is limited time to build something like this, since the legislation is being considered now), but it does a decent job of including major changes that would impact most people. It is pretty simple and bare-bones, but I am working on a number of features that will hopefully not just educate you as to how much your taxes will change, but why.

Please check it out and let me know what you think. I’d love to hear about stuff I’m missing and, especially, how I could make it more useful for you.

CloudStudio for AWS now available

I’m excited to announce the initial release of a project I’ve been working on – CloudStudio.

Amazon’s AWS provides a ton of great services, but not all of them have easy UIs for testing and interacting with them. As a frequent user of services like Kinesis and Firehose, I’ve often wished I could have an easy way to push a message onto a stream, or take a peek at messages coming in, so I started building a suite of tools to just do that.

This initial release focuses on Kinesis, Firehose, and SQS. You can send messages to all 3, and you can poll messages from Kinesis, all with a simple and easy-to-use GUI. It is available now for Mac for $9.99, but I’m wrapping up work on Windows and Linux versions in the next week or so as well.

It may be starting out simple, but I have some really big plans to make it even more useful in the near future, by doing things like allowing you to stream to Kinesis from a file, tail an S3 file, and more. I’ve made my roadmap open on Trello, and I’ve added a forum to solicit feedback on what features you want.

I want this to be a really powerful tool for AWS users, so I hope you’ll check it out here and give me some feedback on how I can make it more useful for you.

Running a single Mocha test file in VS Code

I’ve been doing a lot of Node development lately, after doing mostly Java the past 10 years. There are lots of comparisons between the two, and I’ve come away with a better understanding of where one is better than the other and what I wish I could take from each one.

One thing I miss about doing Java development is being able to right-click in a single test, run it, and be able to easily debug it. You can get close to this in Node-land, but it is nowhere near as simple or seamless. You can, however, get kind of close with VS Code (which I’m loving more and more every day) by creating a custom launch configuration that lets you debug a single mocha file.

1. Create a launch configuration that only runs the current file

This creates a launch configuration that passes the current file to the mocha command.

2. If you have a mocha.opts, you may need to override it

A lot of projects have a mocha.opts file that has something like this ‘–recursive test/.’

Command-line args should override options in mocha.opts, but it looks like the file specification part does not get overridden. So, what I did was create a dummy mocha-debug.opts that is empty, then point to it in the config:

3. You can now run and debug a single Mocha file.

Betterment substantially increases fees

I recently became familiar with Betterment since my employer switched to them for our 401k provider. I started looking into the services they provided, and became really intrigued by their automated investing and tax-loss harvesting. I’ve usually stuck with Vanguard and their low fees, but with a wrap fee of .15% if you had over $100k invested with them, it was tempting to try Betterment, since in theory, at least, the tax-loss harvesting would more than pay for the additional fees.

Getting all of my investments to them took a fair amount of time and money (though they made the process as easy as possible), and I was excited when I got my emails this morning saying my last 2 big accounts had been received by them.

Less than half an hour later, I got another email titled, innocuously, “New Betterment service plans for 2017”. Reading through the email, they discussed their new options that would allow you to use the services of a CFP, which is odd, given their pitch about automated investing, but not a big deal. Then, tucked down 4 paragraphs is the real reason for the changes:

Each plan will cost a simple, flat rate. Starting June 1, your Digital plan will be 0.25% per year of your average balance.

For accounts with over $100,000 in them, this represents an increase of 67% (from .15% to .25%)! And Betterment tries to make it as low-key as possible that they are increasing fees on us by a huge amount and not offering anything in return. I am really disappointed, both in the increase in fees and in the way they announced it – I had a high opinion of the company before this.

At this point, it looks like WealthFront is a better option. Both do a lot of the same things and offer similar features at a fixed .25% fee, but WealthFront manages the first $10,000 for free and offers a Direct Indexing service that lets you avoid ETF fees, making the combined fee substantially less than Betterment.

Fees should be getting cheaper as companies like this get more assets under management, not going higher by almost 70%, and companies should be more forthright in the way they raise fees.

Combining data from multiple sources with Spark and Zeppelin

I’ve been doing a lot with Spark lately, and I love how easy it is to pull in data from various locations, in various formats, and have be able to query/manipulate it with a unified interface.

Last week, I had a situation at work where I needed to get a list of users (from our mySQL database), filtered with ancillary data from JSON files in S3 and a CSV file that had been sent to me and was sitting on my local machine. Using Spark and Zeppelin, I was able to do this in just a few minutes – analyzing a few GBs of data from multiple sources in multiple formats from my local machine took only a few minutes to execute, too (this approach would work with much larger data also, you just would want to run it on a cluster..).

I wanted to share a simplified, adapted version of that with others to show the power of Spark and Zeppelin. This is a really trivial example with a tiny amount of data, but hopefully it gives an idea of what is possible – and while it may seem kind of gimmicky, I’ve already used something very similar once for an actual business use case, and I can see lots of other situations where it could come in handy as well.

The Task

Marketing has sent us a list of users – some of whom they sent an email, some they didn’t. They want to know how many pages the users who got sent our email viewed versus users who did not. Unfortunately, our analytics data only has the user id, and marketing only has their email address – we’ll have to use the data in our database to bridge them.

Requirements

Zeppelin
mySQL
AWS S3 (You can obviously change this to a local file, if you want)

Files

I’ve uploaded samples for all of the assets you need, as well as the Zeppelin notebook itself:

SQL file for creating the users table and the users in it
JSON file with analytics data to be loaded from S3
Email list with all of the users that marketing emailed in CSV format
Zeppelin file that has all of the code

I’ve posted screenshots in this post, if you’d like the code, grab the Zeppelin file.

Step 1: Imports

We need to import a mySQL driver, the AWS SDK (so we can load data from S3), and the databricks library that can create RDDs from CSV files.

dependencies

Step 2: Load users from mySQL

We want to create a DataFrame from our users table in our database. Note that it will parse the schema and turn it into a DataFrame with similar column names as are in the table. We now have all of our users and their ids/names/emails available to us.

database

Step 3: Load JSON file from S3

Spark is really awesome at loading JSON files and making them queryable. In this case, we’re doing a little extra work to load it from S3 – just give it your access key, secret key, and then point it at the right bucket and it will download it and turn it into a DataFrame based on the JSON structure.

Now we have a record of all events from all of our users.

events-s3

Step 4: Load the email list CSV from our local filesystem

We have a simple CSV file in the format of <email address>,<promo email sent: true|false>. Let’s read it in now, too, so we know who got an email and who didn’t. In this case, our CSV has a header file that Spark will use to create the DataFrame columns, and it will attempt to infer the schema based on the data in the file – we can also pass in an explicit schema if we need to.

emaillist-csv

Step 5: Query

Now, let’s write a query that joins them all together and tells us who got an email and how many events they produced. As you can see, this looks like any other SQL query, only it is pulling data in from a mySQL database, a JSON file we pulled from S3, and a local CSV file.

query-1

Step 6: Graph

We can even use the %sql interpreter in Zeppelin to write a query without any Scala code and graph the results:

query-2

Conclusion

As you can see, the combination of Spark and Zeppelin is incredibly powerful. Data exploration and complex reporting – even with terabytes of data spread across multiple data sources – is fairly easy to do.