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Data Manipulation at Scale: Systems and Algorithms

University of Washington

4.3 (688 ratings) | 49K Students Enrolled

Course 1 of 4 in the Data Science at Scale Specialization

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams

Skills You'll Learn

Relational Algebra, Python Programming, Mapreduce, SQL

Reviews

4.3 (688 ratings)

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DK

Jan 24, 2016

Good! I like the final (optional) project on running on a large dataset through EC2. The lectures aren't as polished and compact as they could be but certainly a very valuable course.

LG

Jun 20, 2017

Comprehensive and clear explanation of theory and interlinks of the up-to-date tools, languages, tendencies. Kudos and thanks to Bill Howe.\n\nHighly recommended.

From the lesson

Relational Databases and the Relational Algebra

Relational Databases are the workhouse of large-scale data management. Although originally motivated by problems in enterprise operations, they have proven remarkably capable for analytics as well. But most importantly, the principles underlying relational databases are universal in managing, manipulating, and analyzing data at scale. Even as the landscape of large-scale data systems has expanded dramatically in the last decade, relational models and languages have remained a unifying concept. For working with large-scale data, there is no more important programming model to learn.

Optimization: Physical Query Plans5:29

Optimization: Choosing Physical Plans4:54

Declarative Languages5:24

Declarative Languages: More Examples4:53

Views: Logical Data Independence5:07

Taught By

Bill Howe
Director of Research

Transcript

[MUSIC]

All right, so how do we use these views?

Well, an example of why you might want to do this, a high end, let's define

a high end store as a store that sold some product for a price over 1000.

All right, so a high end boutique store.

Then for each customer, find all the high end stores that they visit and

return a set of customer name high end store pairs.

In this case, this is a fairly complicated query.

But now we've got a definition of high end store that others can

use in their own queries if they wanna access.

Actually, I didn't do a very good job of explaining this.

This is actually exercising the view we just defined in the previous slide.

So this is a new query that exercises this store price view.

Okay. I was actually going one step further.

And I wanted to define a new view that used, where this query was the definition

because I thought the notion of a high end store might be something useful.

And of course you can do that.

You can stack views on top of views on top of views on top of views.

All right, so how do we use a view?

Well, as I said all you have to do is

reference a view in the query just like it's a table.

And so here you wanna define the notion of a high-end store and

we say well that's any store that has sold some product over a $1000.

And for each customer we may wanna find all the high-end stores that they visited.

Well, being able to directly reference the store price relation

that we defined in the previous slide, this view helped simplify this query.

So you can just write a query that directly accesses

that view as if it was a table.

And okay.

So, how is this actually evaluated?

Well, that's actually, oops.

That's actually what's really fantastic about databases,

is that this query will just be folded together with the view definition and

passed to the database, where the whole thing is optimized in one go.

So you don't need to worry about the difference between having a stack

of five Us and all being folded together in one query.

The database doesn't care.

It's gonna translate the whole thing into one big query, exchange that for

an algebraic expression, and

that do the normal optimization procedure to come up with the best possible plan.

So it's basically like free abstraction.

It simplifies things for the programmer without any kind of performance cost.

Now, you can actually get better performance than writing the whole

thing by hand.

It's equivalent to writing the whole thing by hand, you don't pay a penalty.

But you can actually do better than that with views and

in some cases buy materializing views.

And we're not going to talk too much about that because they're sort of is very

specific to databases and we don't see it quite as often in this broader context

of Data Science that we're trying to talk about.

But, it's a good trick and once you have the mechanism to store views,

you can essentially cache the results and that's what we call materialization, okay?

And so one final point I wanna make.

Another key idea of databases is indexes.

And while indexes are certainly not unique to databases, what I think databases have,

perhaps uniquely, is a fantastic platform for building them and

applying them and using them.

Right? So if you are in a general purpose

programming language, if you're working in Python or you're working in R, and

you come up with a pipeline to process some data, and you decide boy it'd be

a lot better if I sorted my data by customer

last name before accessing it, because that would make things more efficient.

That's like building an index.

But you have to rewire all your code in order to take advantage of that index.

The key thing about databases is that indexes are easily built and

automatically used by the optimizer when appropriate.

So, all you have to do is write in an expression like this, create index on

some column of some table.

And here I've switched the schemas yet again on you.

And the database now understands that this index exists and

will use it whenever it's appropriate.

And you can see this in those plans.

I talked about using the explain to figure out the algebraic expression

in the plan and actually look at it to reason about the performance.

You'll see it selecting to use

an index scan as opposed to a regular scan when these indexes exist.

Okay.

And sort of going in reverse order here, this top bullet is just saying that

one of the areas where databases shine is sort of these needle in haystack problems,

for which indexes are critical.

You can ignore this point about old style scalability, I skipped that point.

It's not quite as relevant for data science.

So the last key idea I want to convey about databases is that of indexes.

So while indexes are certainly not unique to databases,

databases are perhaps unique as a platform that can make them very easy to apply and

deploy and automatically take advantage of.

And so databases are especially, but not exclusively,

effective at needle in the haystack problems.

Looking up individual records or small amounts of records from large data sets.

They do other things very well too but

this is one thing that they're quite good at.

And the reason is that you can apply indexes.

This second bullet needs a little bit of context, but

what I mean hear is that if you're trying to write code to do this yourself

in some programming language like python or C or R, you're

going to be a slave to what sizes of data fit into main memory as we've said before.

Now you can absolutely be clever and

start bringing in one chunk of data at a time in memory,

processing it, putting it out to disk and bringing in the next set and so on.

But the code will very quickly become very very complex.

This is something that databases already know how to do.

And so your query will always finish regardless of database size,

as long as it fits on disk, right?

It doesn't matter how much memory you have available, it'll eventually finish.

It may not be that fast, but it'll finish.

They already know how to take advantage of main memory in this optimal way.

And you know,

it's not easy, right it's a pain in the butt to try to code that yourself.

Okay.

So effective use of the memory hierarchy, effective use of indexes.

These are things the databases can do well.

It's a great platform for applying these tricks.

And so finally this what I mean here is that the indexes are easily built and

automatically used by the optimizer.

So to create an index you can write a statement like this.

Here I've changed the schema on you once again.

But here we're sort of filtering on genetic sequences, and

if I create this index then this query will- You know,

this very simple query is looking for all sequences that match a particular value.

It will automatically take advantage of that index if it's there.

You don't have to tell it to do anything.

You write the exact same query.

[MUSIC]

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