The Hadoop "Zoo"

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Del curso dictado por University of California San Diego

Hadoop Platform and Application Framework

calificaciones

University of California San Diego

Hadoop Platform and Application Framework

calificaciones

This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment. In the assignments you will be guided in how data scientists apply the important concepts and techniques such as Map-Reduce that are used to solve fundamental problems in big data. You'll feel empowered to have conversations about big data and the data analysis process.

De la lección

Hadoop Basics

Welcome to the first module of the Big Data Platform course. This first module will provide insight into Big Data Hype, its technologies opportunities and challenges. We will take a deeper look into the Hadoop stack and tool and technologies associated with Big Data solutions.

Hadoop Stack Basics4:15

The Apache Framework: Basic Modules3:32

Hadoop Distributed File System (HDFS)5:33

The Hadoop "Zoo"5:23

Hadoop Ecosystem Major Components11:27

Conoce a los instructores

Natasha Balac
Director, Predictive Analytics Center of Excellence (PACE)
San Diego Supercomputer Center
Paul Rodriguez
Research Programmer
San Diego Supercomputer Center (SDSC)
Andrea Zonca
HPC Applications Specialist
San Diego Supercomputer Center (SDSC)

We have talked about the bottom of the frame work, of the stack.

We've talked about HDFS and we talked about yarn or map reduce version 2.0.

As you can see on top of these things are several pieces of architecture.

And they have evolved over time.

So let's look at the history and

look at how the entire ecosystem has evolved and grown over time.

As you might have noticed,

a lot of these applications have different kinds of funny names.

And so, how can we figure out the entire zoo, and

how can we figure out which one of these applications does what?

What is it used for?

Is it a SQL-like environment?

Is it where we submit jobs?

Is it how we access our data?

What can we do with all of these applications?

So let's look into each one of them in details.

So we talked about that the roots of Hadoop go way back into Google's Bigtable

and different kinds of applications that started working around that idea of

how do we scale and distribute and process very large amounts of data?

So here, we have the original Google stack as some people refer to it.

And what they did, is they started with a Google file system.

They thought that it would be a great idea to distribute a large amount of pretty

cheap storage and try and put a lot of data on there.

And come up with some framework that would allow us to process all that data.

So they had their original MapReduce, and they were storing and

processing large amounts of data.

Then they said, well that was really great, but we would really like to be

able to access that data and access it in a SQL like language.

So they built the SQL gateway to adjust the data into the MapReduce cluster and

be able to query some of that data as well.

Then, they realized they needed a high-level specific language to

access MapReduce in the cluster and submit some of those jobs.

So Sawzall came along.

Then, Evenflow came along and

allowed to chain together complex work codes and coordinate events and

service across this kind of a framework or the specific cluster they had at the time.

Then, Dremel came along.

Dremel was a columnar storage in the metadata manager that allows us to manage

the data and is able to process a very large amount of unstructured data.

And then of course, you needed something to coordinate all of this.

So Chubby came along as a coordination system that would manage all of

the products in this one unit or one ecosystem that

could process all these large amounts of structured data seamlessly.

Well, then we can see that Facebook's stack looks very similar.

You have Zookeeper that orchestrates things.

You have HBase, you have Hive and Databee, and

then we have Scribe that is used for ingestion of large data set.

Then, if we move on and look at the Yahoo stack,

you can see that they will use some of the same components but they have something

called Data Highway and Oozie and then HCatalog for the metadata catalog.

So things are still very similar.

They are still used for the same purposes.

However, they might have different names

because they have somewhat different implementations of these tools.

Moving on, we can see that LinkedIn has their own version of this stack.

Again, you can see that there's some of the same components, and

then there's some specific versions of these tools that these organizations

developed on their own.

So you can see that there's a pattern that emerges across all these stacks

the different organizations use it.

And now we can see that we come down to the Cloudera's distribution for Hadoop,

and we can see which one of the pieces of the system Cloudera has.

So you can see that in Cloudera's distribution for Hadoop, or

Cloudera Stack as we call it We have Sqoop and Flume for ingestion.

We use HBase for the common store.

We have Oozie as the coordination and the workflow engine.

We use Pig and Hive for high level languages and querying some of the data.

And then we use Zookeeper as a coordination service on bottom

of this stack.

So we decided to use Cloudera's distribution for

Hadoop because they had this really excellent quick start VM,

that we, you guys have already downloaded in the previous class, and

we can do all kinds of experiments on it, without having to spend the time

either begging our IT guys to install a full installation of the Hadoop stack.

Or having to spend time doing it ourself.

This VM is structured in a way that enable us to run all these different services and

learn how they work without a whole lot of back end work involved.

I know that some of you may be have had problems with a VM and

if you don't have enough memory it might be really slow.

Cloudera does have the live version where you can log in and do some of these

experiments on your own without having to have a VM running on your engine.

So there's some of the write ups in our reading sections of this class as well.

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