When it comes to machine learning, there are a lot of tools out there that promise to be the best and help you sort through all that big data. But if you want one of the best, and you want to work with Python coding to help you sort through all the data, then Apache Spark is the best option for you. Let’s take a look at how Apache Spark and Python can work together to make machine learning and big data a dream for your analysis.
Using Apache Spark and Python
If you want to spend some time working on your big data with the help of machine learning, then you need to learn how to use Apache Spark and Python. Apache Spark is easy to use, fast, and a general engine that is perfect for processing any big data that you would like. Since many companies want to spend their time learning more about the big data they gather and see what patterns are inside, learning how to work with Apache Spark is important as well.
There are many reasons why Apache Spark is a great option to use for machine learning and big data. For example, it comes with several modules already built-in that are for graph processing, machine learning, SQL, and streaming. This technology includes lots of skills for data engineers and data scientists when you want to handle machine learning, feature extraction, and so much more.
How to Install Apache Spark
The first thing that we need to work on is installing Spark and getting it to work on our computers. First, we need to check whether our system has the right pre-requisites to get this on. Spark is done in a Java Virtual Machine, or JVM, environment, and is written in the Scala Programming language. This means that you should double-check to see whether the Java Development Kit is installed on your computer.
If this is not on your system, you can visit the JDK website and choose the version that you want. Choose the latest version to make sure that it will work for your needs. Then it is time to download spark.
You will want to download pyspark with a pip. This is easy and is similar to installing any other package that you may have used in the past. You can use a command to help with this. In particular, use the command below:
$ pip install pyspark
Another option is to just head right to the Spark download page. You should just choose the default options that are found in the first three steps and when you get to step 4, you can find the link. Click on that link to download it and wait until it is all done.
Setting Up Spark
Once the download is complete, you will want to take some time to set it all up. You should be able to look at your download folders and see that Spark is inside. Click on the files and give it some time to open up if it is still necessary.
Take some time here to look around and see what is inside there. Get familiar with the different buttons that are there and make any adjustments based on some of your preferences. When you open up Spark, it will ask you some questions about how you would like the system to set things up for you. Take a few moments to work on this to make things easier.
Each company will want to set up Spark differently. Once you have it set up the way that you would like, you can install Python into this as well. This will help you work with the Python language, which is one of the best coding languages when it comes to working on big data and machine learning. You can also choose any Python libraries that you would like.
Working on the Data
After you take some time to work through Apache and getting it on your system, it is time to work with the data. We are going to explore a small set of data here and you may find that this is oversimplified for what Spark can do, but it still gives us an idea of the steps we need to take to make it happen.
Loading and Exploring the Data
Even though you most likely know at least a little bit of the data, it is time to look through it more. We need to set up the Jupyter Notebook to help us get this done. You won’t need to use the ishell for this one, but we can build up our application inside the Notebook. All of this will be installed so it won’t take that much. The code you can use to help finish this one up includes:
# Import findspark
# Initialize and provide path
# Or use this alternatie
One tip to remember here is that if you have no idea if you have set the path correctly or where you installed Spark on the pc, you can use the code “findspark.find() to help detect where this program is.
Creating the First Program in Spark
Now it is time to work on creating our first program in Spark. We are going to have to do a little bit of work to make this happen, but it will be easier than you think to get it set up. The code you need to create a program in spark includes:
# Import SparkSession
from pyspark.sql import SparkSession
#build the SparkSession
spark = SparkSession.builder \
.appName(“Linear Regression Model”) \
.config(“spark.executor.memory”. “lgb”) \
sc = spark.sparkContext
Loading the Data
Now it is time to go through and load up the data that you want to use. The one we work with is the California Housing data set which has 20,640 observations with nine different variables. This will help you to learn more about the data. You just need to work on loading the data to get it in. The code that you need to help with this is:
# Load in the data
rdd = sc.textFile(‘/Users.yourName/Downloads/CaliforniaHousing/cal_housing.data’)
#Load in the header
header = sc.textFile(‘/Users/yourName/Downloads/CaliforniaHousing/cal_housing.domain’)
Explore the Data
Once you have your data set all ready to go, it is time for you to go through and explore the data. You can then use Spark, and the help of the Python language, to help you explore all of these data points and see the patterns. You can use any algorithm of machine learning that you want to take a look at this data.
This is where you need to use a little precaution and try out a few different things. You will need to use your skills with Python and machine learning to pick out from several algorithms and use the knowledge that you already have about the data to determine which one gives the best results. This may take some trial and error along the way, but Apache Spark and Python, along with the tools that come with Python, can help you get all of this done.
There are so many cool things that you can do with machine learning and as more businesses find that understanding big data is the key to getting ahead in their industries, you will find that having the right tools to sort through this data is important. Python can help by providing a simple language to get things done, along with many great tools and libraries. And Apache Spark can provide you with a way to take all those other tools and put them together with some strong machine learning along the way.
When all of this comes together, you can end up with some amazing insights out of all that big data that can help propel your business together.