Some High-Level Processing Operations in Big Data Pipelines

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Big Data Integration and Processing

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University of California San Diego

Big Data Integration and Processing

1333 ratings

Course 3 of 6 in the Specialization Big Data

At the end of the course, you will be able to: *Retrieve data from example database and big data management systems *Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications *Identify when a big data problem needs data integration *Execute simple big data integration and processing on Hadoop and Spark platforms This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.

From the lesson

Processing Big Data

This module introduces Learners to big data pipelines and workflows as well as processing and analysis of big data using Apache Spark.

Big Data Processing Pipelines7:06

Some High-Level Processing Operations in Big Data Pipelines8:24

Aggregation Operations in Big Data Pipelines5:15

Typical Analytical Operations in Big Data Pipelines10:43

Meet the Instructors

Ilkay Altintas
Chief Data Science Officer
San Diego Supercomputer Center
Amarnath Gupta
Director, Advanced Query Processing Lab
San Diego Supercomputer Center (SDSC)

In data integration and processing pipelines,

data goes through a number of operations, which can apply

a specific function to it, can work the data from one format to another,

join data with other data sets, or filter some values out of a data set.

We generally refer to these as transformations, some of which can also

be specially named aggregations as you have seen in Amarnath's earlier lectures.

In this video we will reveal some common transformation operations that we see

in these pipelines, some of which, we refer to as data parallel patterns.

After this video you will be able to list common data

transformations within big data pipelines, and design

a conceptual data processing pipeline using the basic data transformations.

Simply speaking, transformations are higher order functions or

tools to convert your data from one form to another, just like

we would use tools at the wood shop to transform logs into furniture.

When we look at big data pipelines used today,

map is probably the most common transformation we find.

The map operation is one of the basic building blocks of the big data pipeline.

When you want to apply a process to each member of a collection,

such as adding 10% bonus to each

person's salary on a given month a map operation comes in very handy.

It takes your process and understand that it is required to perform

the same operation or process to each member of the set.

The figure on the left here shows the application

of a map function to data depicted in grey color.

Here colors red, blue, and yellow are keys to identify each data set.

As you see, each data set is executed separately even for the same colored key.

The reduce operation helps you then to collectively apply the same

process to objects of similar nature.

For example, when you want to add your monthly spending in different categories,

like grocery, fuel, and dining out, the reduce operation is very useful.

In our figure here on the top left,

we see that data sets in grey with the same color

are keys grouped together using a reduced function.

Reds together, blues together, and yellows together.

It would be a good idea to check out the Spark word count hands-on to see

how map and reduce can be used effectively for getting things done.

Map and reduce are types of transformations that work on a single

list of key and data pairings just like we see on the left of our figure.

Now let's consider a scenario where we have two data sets identified

by the same keys just like the two sets and colors in our diagram.

Many operations have such needs where we have to look at all the pairings of

all key value pairs, just like crossing two matrices.

For a practical example Imagine you have two teams,

a sales team with two people, and an operations team with four people.

In an event you would want each person to meet every other person.

In this case, a cross product, or

a cartesian product, becomes a good choice for organizing the event and

sharing each pairs' meeting location and travel time to them.

In a cross or cartesian product operation, each data partition gets

paired with all other data partitions, regardless of its key.

This sometimes gets referred to as all pairs.

Now add to the cross product by just grouping together the data

partitions with the same key, just like the red data.

And the yellow data partitions here.

This is a typical match or join operation.

As we see in the figure here, match is very similar to the cross product,

except that it is more selective in forming pairs.

Every pair must have something in common.

This something in common is usually referred to as a key.

For example, each person in your operations team and

sales team is assigned to a different product.

You only want those people to meet who are working on the same product.

In this case your key is product.

And you can perform and match operation and

send e-mails to those people who share a common product.

The number of e-mails is likely to be less than when you performed a cartesian or

a cross product, therefore reducing the cost of the operation.

In a match operation, only the keys with data in both sets get joined,

and become a part of the final output of the transformation.

Now let's consider listing the data sets with all the keys,

even if they don't exist in both sets.

Consider a scenario where you want to do brainstorming sessions

of people from operations and sales, and

get people who work on the same products in the same rooms.

A co-group operation will do this for you.

You give it a product name as they key to work with and

the two tables, the sales team and operations team.

The co-group will create groups which contain team members

working on common products even if a product doesn't exist in one of the sets.

The last operation we will see is the filter operation.

Filter works much like a test

where only elements that pass a test are shown in the output.

Consider as a set that contains teams and a number of members in their teams.

If your game requires people to pair up,

you may want to select teams which have an even number of members.

In this case, you can create a test that only passes the teams which have

an even number of team members shown as divided by 2 with 0 in the remainder.

The real effectiveness of the basic transformation we saw here

is in pipelining them in a way that helps you to solve your specific

problem just as you would perform a series of tasks on a real block

of wood to make a fine piece of woodwork that you can use to steer your

ship, which in this case is your business or research.

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