Velocity from Sensor Data - Best Approach?
Welcome to the world of signal processing! (Starting off with a Kalman filter, though, is not very welcoming). 

The first few things to address are in regard to the barometer:

(1) Inevitably, it's going to be noisy, and barometers in particular are known to be noisy. Considering the unfiltered data you posted, I'd say it's reasonable. The spikes are completely normal, especially being that your calculating the derivative from the barometer data. If you want to try to smooth the barometer data before passing it to the Kalman filter, you could apply a nth order low pass filter to the sampled data. This, of course, would add more lag to your calculations - it's really a trade off. 

(2) Looking at the unfiltered data and the velocity calculated from the Raven, it appears that there might be an issue. Starting at time 0, the unfiltered data and the Raven data is nearly identical. Once the rocket reaches 180ft/s, though, the filtered data appears to diverge. After it passes the global maxima and hits 180ft/s again, the two data outputs start to converge. This makes me suspect that your sampling rate may be too low. A hard-core, by the book, DSP engineer will probably start crying when he reads this, but consider this: at a velocity of 180ft/s with a sample rate of 20S/s, each sample corresponds to a change in position of 9ft. So, at best, you have a resolution of 9ft per second. This can pose at least two problems. (1) The barometer has to continuously update temperature and pressure readings in order to calculate an accurate altitude. I only glanced at the datasheet, but it seems like the temperature (and I think one other parameter) is updated at a slower rate than the pressure sampling - that is, there's an oversampling factor for the pressure measurement. Hence, your calculation may start to diverge from reality. And as the rocket's velocity increases, this problem may be exacerbated. Of course, temperature changes at rate of about 9.8C/1000m, so it may not be that significant of an issue. Just wanted to throw it out there.  (2) Again, at 180ft/s with a 20Hz sampling rate, you basically have a resolution of 9ft/S. This isn't necessarily bad, but one thing to keep in mind is that a Kalman filter is predictive; it predicts the state of the system 1/20 of a second into the future. If your underlying model is off, this could lead to slower convergence and larger corrections that would make the filtered data look "jerky."

Just one more thing before we get into the filter...

It's worth considering how you're implementing this on the Arduino. There shouldn't be any issues with computation time that could result in significant lag. If your only sampling the altitude at 20Hz and then feeding it to the filter at the same speed, you should be good to go. The only issue I can foresee is if you're using the Arduino Libraries. If you are, you'll probably run into bottlenecks and missed deadlines.

Anyway, the primary issue you're having - in my opinion - is not related to computational speed or hardware issues. Instead, I suspect it's your implementation of the filter. 

One thing that stood out to me were your values of R and Q. R is the measurement noise covariance, and Q is the process noise covariance. Covariance ranges between [-1, 1]. Your Q = 5. I quickly scanned the link you provided, so I might have missed a simplification they did. I've seen in some cases that the covariance is simplified to a proportional expectation value and left unnormalized, similar to what's seen in Gram matrices. 

Regardless, here's some insight that may be useful:

The Kalman filter is very much dependent on how you model your system. 

To illustrate this, let's consider an example analogous to your rocket. (I came across this example at some point, but I don't recall the source):

Say you want to measure the water level in a bucket, but you have a sensor that gives you noisy data. And of course, to filter the data, you decide to use a 1-D Kalman filter. 

We assume that we have a pretty accurate model of our system, so we'll let Q = 0.001. We iterate through the filter, and we may get data that looks like this: 


The filter works fairly well.

But what if our model happened to be off - that is, it doesn't reflect what's really happening? 

In the image above, the water level was constant. In reality, perhaps the tank was filling at a constant rate. What happens now if we use the same filter?



Clearly, the filter is diverging. The lag, as you call it, isn't really lag or a delay in the sense that the computation is slow. (In some ways it is a delay, but only in the mathematical sense that the filter is slowly converging). The "lag" effect we're seeing is due to our model. In essence, the filter we designed thinks our model is so good (as indicated by our low Q) that it gives greater weight to our model of the system. As you probably guessed, one way we can fix this problem is by increasing Q. So, let Q = 0.1:



You can keep increasing Q, and when Q = 1, your filtered data should actually match your measurement data. 

This, though, really ins't the best way. Ideally, you need to start with a relatively accurate model. Q can be thought of as a measure of the error in your model, and you want to minimize error. At this point, you'll inevitable need to start work with matrices. This won't increase "lag." In fact, it should help your filtered data converge quicker.

There is one more thing to note. Kalman filters prefer linear data (speaking generally here). Look what happens when you feed in nonlinear data:



To accurately and effectively apply a Kalman filter to non-linear data without such pronounced "lag",  you need to use what is known as an extended Kalman filter - which basically linearizes your data.

Before you go down that route, which would probably be overkill, I would suggest improving your model used in your Kalman filter to better reflect it's true state. This means ditching the simplified filter you found and going into the matrix math. 

In any case, I hope this helped. Feel free to ask any questions. 

(Another method just crossed my mind that may be a simpler approach than a Kalman filter. I'll add it tomorrow).