This is a problem that everyone wrestles with when they start with (and indeed when they are experienced with) DynamoDB.
Pricing and throughput
Let's start with how DynamoDB is priced (its related - honestly). Ignoring the free tier for a moment, you pay $0.25 per GB per month for data at rest. You also pay $0.47 per Write Capacity Unit (WCU) per month and $0.09 per Read Capacity Unit (RCU) per month. Throughput is the number of WCUs and RCUs on your table. You have to specify throughput up front on your table - the volume of writes and reads you can perform on your table is limited by your throughput provision. Pay more money and you can do more reads and writes per second. The exact details of how DynamoDB partitions tables can be found in this answer.
Keys
Now we need to consider table partitioning. Tables must have a primary key. A primary key must have a hash key (aka a partition key) and may optionally have a sort key (aka a range key). DynamoDB creates partitions based on your hash key values. Within a partition key value the data is sorted by range key, if you have specified one.
Data Access
If you have the exact primary key (hash key and range key if there is one), you can instantly access an item using GetItem. If you have multiple items to get, you can use BatchGetItem.
DynamoDB can only 'search' data in two ways. A Query can only take data from one partition in one call, because it uses the partition key (and optionally a sort key) it is quick. A Scan always evaluates every item in table, so its typically slow and doesn't scale well on large tables.
Throughput distribution
This is where is gets interesting. DynamoDB takes all the throughput you have purchased and evenly spreads it over all of you table partitions. Imagine you have 10 WCUs and 10 RCUs on your table, and 5 partitions, that means you have 2 WCUs and 2 RCUs per partition. That's fine if you access each partition evenly, you get to use all of your purchased throughput. But imagine you only ever access one partition. Now you've purchased 10 WCUs and RCUs but you are only using 2. Your table is going to be much slower than you thought. One option is to just buy more throughput, that will work, but its probably not very satisfactory to most engineers.
Uniform Access v Natural Access
Based on the above we know we want to design a table where each partition gets accessed evenly. However, in my experience people get too hung up about this, which is not surprising if you read the article I just linked (which you also linked).
Remember that partition keys is what we use in a Query to get our data fast, and avoid regular Scans. Some people get too focussed making their partition access perfectly uniform, and end up with a table they can't query quickly.
The answer
I like to refer to Best Practices for Tables guide. And particularly the table where it says User ID is a good partition key so long many user access your application regularly. (It actually says where you have many users - which is not correct, the size of the table is irrelevant).
Its a balance between uniform access and being able to use intuitive, natural queries for your application, but what I am saying is, if you are new to DyanmoDB, the right answer probably is to design your table based on intuitive access. After you've done that successfully, have a think about uniform access and hot partitions, but just remember access doesn't have to be perfectly uniform. There are various design patterns to achieve both intuitive and uniform access, but these can be complicated for those starting out and in many cases can probably discourage people using DynamoDB if they get too focussed on the uniform access idea.
Tips
Most applications will have users. For most queries, in most applications, the most common query you will do is get data for a user. So the first option for most application's primary partition key will often be a user id. That's fine, as long as you don't have a few very high hitting users and many users that never log in.
Another tip. If your table is called vegetables, your primary partition key will probably be vegetable id. If your table is called shoes, your primary partition key will probably be shoe id.
Most applications will have many items for each user (or vegetable or shoe). The primary key has to be unique. A good option often is to add a date range (sort) key - perhaps the datetime the item was created. This then orders the items within the user partition by creation date, and also gives each item a unique composite primary key (i.e. hash key + range key). It's also fine to use a generated UUID as a range key, you wont use the ordering it gives you, but you can then have many items per user and still use the Query function.
Indexes are not a solution
Aha! But I can just make my partition key totally random, then apply an index with a partition key of the attribute I really want to query on. That way I get uniform access AND fast intutive queries.
Sadly not. Indexes have their own throughput and partitioning, separate to the table the index is built on. Just imagine indexes as a whole new table - that's basically what they are. Indexes are not a work around to uneven partition access.
Finally - your schema
Primary Key
Hash Key: Event ID
Range Key: None
Global Secondary index
Hash Key: Calendar ID
Range Key: startTimestamp
Assuming Event ID is uniformly accessed, it would be a great hash key. You would really need to describe how your data is distributed to discuss this much more. Other things that come in to play are how fast you want queries to work and how much you are willing to pay (e.g. secondary indexes are expensive).
And your queries:
Get an event by ID
GetItem using Event ID
Get all events where calendarId = x and ownerId = y
Query by GSI parition key, add a condition on ownerId
Get all events where startTimestamp is between x and y and calendarId = z
Query by GSI parition key, add a condition on range key
