How to Manage Big Data with 5 Python Libraries?

Date: Oct 25, 2022

Related Tags：1. Advantages of Using Python for Data Analytics
2. How To Get Python

Abstract: Python is really everywhere these days. Although many gatekeepers argue whether a person is really a software developer if they don't code in a language harder than Python, it's still everywhere.

Python is really everywhere these days. It's still everywhere, although many gatekeepers argue that a person is really a software developer if they don't code in a harder language than Python.

Python is used for automation, managing websites, analyzing data and processing big data. As data grows, the way we manage it increasingly requires adjustment. We are no longer limited to just using relational databases. It also means that there are now more tools for interacting with these new systems, such as Kafka, Hadoop (HBase to be specific), Spark, BigQuery, and Redshift (to name a few).

Each of these systems utilizes concepts such as distributed, columnar structures, and streaming data to provide information to end users faster. The need for faster, updated information will drive data engineers and software engineers to take advantage of these tools. That's why we want to provide a quick introduction to some Python libraries to help you.

BigQuery

Google BigQuery is a very popular enterprise repository that is a combination of Google Cloud Platform (GCP) and Bigtable. This cloud service works well with data of various sizes and executes complex queries in seconds.

BigQuery is a RESTful web service that enables developers to perform interactive analysis on massive datasets in conjunction with Google Cloud Platform. See another example below.


I wrote an article earlier that explains how to connect to BigQuery and start getting information about the tables and datasets that you will interact with. In this case, the Medicare dataset is an open source dataset that anyone can access.

Another point about BigQuery is that it runs on Bigtable. It is important to understand that this repository is not a transactional database. Therefore, it cannot be considered an online transaction processing (OLTP) database. It is designed for big data. So it works in line with the processing of petabytes (PB) scale datasets.

Redshift and Sometimes S3

Next up is Amazon's popular Redshift and S3. Amazon S3 is essentially a storage service for storing and retrieving large amounts of data from anywhere on the internet. With this service, you only pay for the storage space you actually use. Redshift, on the other hand, is a well-managed data warehouse that can efficiently handle petabytes (PB) of data. The service uses SQL and BI tools for faster queries.

Amazon Redshift and S3 work with data as a powerful combination: Using S3 can upload large amounts of data to a Redshift warehouse. This powerful tool is very handy for developers when programming in Python.

Here is a script that selects a basic connection using psycopg2. I borrowed Jaychoo code. However, this again provides a quick guide on how to connect and get data from Redshift.

PySpark

Let's leave the world of data storage systems and look at tools that help us process data quickly. Apache Spark is a very popular open source framework that can perform large-scale distributed data processing, and it can also be used for machine learning. This cluster computing framework mainly focuses on simplifying analysis. It works with Resilient Distributed Datasets (RDDs) and allows users to handle the management resources of Spark clusters.

It is often used in conjunction with other Apache products such as HBase. Spark will process the data quickly and then store it into tables set up on other data storage systems.

Sometimes, installing PySpark can be a challenge because it requires dependencies. You can see that it runs on top of the JVM and therefore requires Java's underlying infrastructure to run. However, in the age of Docker, it is more convenient to experiment with PySpark.

Alibaba uses PySpark to personalize web pages and deliver targeted ads - just like many other large data-driven organizations.

Kafka Python

Kafka is a distributed publish-subscribe messaging system that allows users to maintain message sources across replicated and partitioned topics.

These topics are basically logs that receive data from clients and store it in partitions. Kafka Python is designed as the official Java client integrated with the Python interface. It works best with new agencies and is backwards compatible with all older versions. Programming with KafkaPython requires both a reference consumer (KafkaConsumer) and a reference producer (KafkaProducer).

In Kafka Python, these two aspects coexist. KafkaConsumer is basically an advanced message consumer that will be used as the official Java client.

It requires the agency to support the group API. KafkaProducer is an asynchronous message producer that also operates very similar to a Java client. Producers can be used across threads without issue, while consumers require multi-threading.

Pydoop

Let's get this out of the way. Hadoop itself is not a data storage system. Hadoop actually has several components, including MapReduce and the Hadoop Distributed File System (HDFS). So, Pydoop is on this list, but you need to pair Hadoop with other layers such as Hive for easier data processing.

Pydoop is the Hadoop-Python interface that allows interacting with HDFS API and writing MapReduce jobs using pure Python code.

This library allows developers to access important MapReduce functionality such as RecordReader and Partitioner without knowing Java.

Pydoop itself is probably a little too basic for most data engineers. Most of you will likely write ETLs in Airbow that run on top of these systems. However, it's good to have at least a general understanding of your work.

Where to start?

Managing big data will only get more difficult in the years to come. Thanks to ever-increasing network capabilities—Internet of Things (IoT), improved computing, and more—the data we get will continue to grow like a torrent.

Therefore, if we want to keep pace, it is necessary to understand some of the data systems and libraries that can be used to interact with these systems.