Hadoop framework is written in Java language, but it is entirely possible for Hadoop programs to be coded in Python or C++ language. This implies that data architects don’t have to learn Java if they are familiar with Python. World of analytics doesn’t have many Java programmers (lovers!), so Python comes across as one of the most user-friendly, easy to learn, flexible language, and yet extremely powerful for end-to-end advanced analytics applications. We can write programs like MapReduce in Python language, without the need for translating the code into Java jar files. The first order of business is to check out the Python frameworks available for working with Hadoop:
- Hadoop Streaming API
- Dumbo
- Mrjob
- Pydoop
- Hadoopy
Before we explore industry use cases where Python is used with Hadoop, let’s make a distinction between these two technologies. Hadoop is a database framework, which allows users to save, process Big Data in a fault-tolerant, low latency ecosystem using programming models. However, Hadoop has recently developed into an ecosystem of technologies and tools to complement Big Data processing.
On the other hand, Python is a programming language and it has nothing to do with the Hadoop ecosystem. Python is an object-oriented language, similar to C++ or Java, but is used for a variety of applications like web development, advanced analytics, artificial intelligence, natural language processing, etc. Python is a flexible language with an abundance of resources and libraries, and it concentrates on code productivity and readability.
With a choice between programming languages like Java, Scala, and Python for the Hadoop ecosystem, most developers use Python because of its supporting libraries for data analytics tasks. The majority of companies nowadays prefer their employees to be proficient In Python, because of the versatility of the language’s application; and they use Hadoop Streaming API (preferably for text processing) along with other such frameworks to deal with Big Data problems using Python language. Hadoop Streaming API is a utility that goes along with Hadoop Distribution. Hadoop streaming allows users to create and execute Map/Reduce jobs with any script or executable as the mapper or/and the reducer.
In this article, we have highlighted several examples of how tech companies are using Hadoop with Python.
- Facebook Face Finder Application
Facebook is leading research and development in the discipline of image processing and it processes huge amounts of Image-based unstructured data. Facebook enables HDFS to store and extract this enormous data, and it uses Python as the backend language for most of its Image Processing applications such as Image resizing, facial image extraction, etc.
Therefore Facebook uses Python as a common platform for its image-related application and uses Hadoop Streaming API to access and edit the data.
- Quora Search Algorithm
Quora manages an incredible amount of textual data using Hadoop, Apache Spark, and several other data-warehousing technologies. Since Quora’s back end is developed on Python; this language is used to interact with the HDFS. Hence Quora uses Hadoop with Python to extract Questions upon search or for suggestions.
- Amazon’s Product Recommendation
Amazon has a leading platform that suggests preferable products to existing users based on their search and buying patterns. Their machine learning engine is built using Python and it interacts with their database system, i.e. Hadoop Ecosystem. These two technologies work in coherence to deliver a top-of-the-class product recommendation system and fault tolerant database interactions.
Multiple disciplines have inducted the use of python with Hadoop in their application. This is because Python is a popular language with various available features for Big Data Analytics. Python programming language is dynamically typed, extendable, portable, and scalable; which makes it a lucrative option for Big Data application based out of Hadoop. Some of the other notable industry use cases for Hadoop with Python are mentioned below:
- Limeroad integrated Hadoop, Python, and Apache spark to create a real-time recommendation system for its online visitors, using their search pattern.
- Images acquired from Hubble Telescope are stored using the Hadoop framework and Python is used for image processing on this database.
- Youtube’s recommendation engine is also built using Python and Apache Spark for real-time analytics.
- Animation companies like Disney use Python and Hadoop for managing clusters for image processing and CGI rendering.
What has changed in Big Data ecosystem post 2018?
For long time, Hadoop was considered as synonym of Big Data especially between 2012 & 2018. Most of the Big Data Software developed on top of Hadoop or complied with Hadoop.
It offers a great framework for big data management with key features like distributed storage (HDFS), distributed processing (Map Reduce), Resource Management (YARN), and it is good for large-scale batch processing tasks that won’t require ACID-compliant data storage. However, it comes with certain challenges for organizations.
- Upfront cost & time for setting up infrastructure
- Real-time processing challenges
- Frequent releases of software versions
- Scale-up & Scale down in a quick manner
- High Maintenance cost
- Data security
- Continuous up-gradation of resources (Hardware, software)
- Difficulty in integration with new data sources due to version issues
However, a significant shift occurred from 2018 onward and many new alternatives gained traction. Leading the pack is cloud-based infrastructure for Big Data management to address the aforementioned challenges.
A. Cloud platforms (GCP, AWS, Azure, Data Bricks, IBM, and Oracle, etc): These platforms offer some of the open-source & proprietary frameworks for distributed storage, parallel processing. Also offer a variety of additional services including Networking, Security, Artificial Intelligence and cognitive services.
B. Distributed Processing Frameworks: Spark, Storm, Flink, Presto, Samza, KUDU, Airavatha, Grid computing (SAS), Tez, Impala, Beam, Apex, etc.
C. Distributed Storages Systems: NoSQL databases like Cassandra used by Facebook, BigTable using by Google, Couchbase used by Paypal/ebay, Druid used by Yahoo/Netflix, DynamoDB used by Amazon, MongoDB used by many small and large enterprises, Redis, HyperTable, Voldemort using by LinkedIn etc. RDBMS (Massive Parallel Processing systems like TeraData, Netezza, Vertika, Snowflake, Redshift, Oracle, Greenplum etc)
These alternatives are rapidly taking share from Hadoop. Most of companies are migrating Big Data to the cloud in recent days and cloud platforms are increasing playing bigger role Big Data Engineering.
Related: What is Big Data Engineering or Modern Data Engineering?
What is the significance of Python in Big Data Engineering?
Even post recent developments in Big Data, Python remains as one of the preferred language by data engineers and developers for building data-intensive projects and performing complex data processing tasks using on-premises and cloud infrastructure. With Python you can do anything in Big Data and manage big data systems like Hadoop, Cloudera, MongoDB and Cloud platforms like AWS, Google cloud services and Microsoft Azure etc.
Python is very versatile and has many in-built libraries, connectors, API’s and frameworks for connecting with various applications, data sources and performing data engineering tasks across the complete stack Python allows data engineers to maintain a high efficacy across the complete project.
A. Python can integrate with most of existing data sources, applications using various connectors and APIs.
Popular Connectors:
- ORACLE – cx_Oracle
- SQL SERVER/RDBMS SYSTEM – pyodbc
- MYSQL – mysql-connector
- SQL ALCHEMY – sqlalchemy
- HDFS – libhdfs3, hdfs3, bite
- SQL LITE – sqlite3
- IMPALA – Impyla
- POSTGRESQL/Amazon Redshift – psycopg2
- PUGSQL – pugsql
- IBIS – Hadoop & SQL Engines
- PANDAS – Text Files (CSV, Text, Delimiter, Excel, JSON Files, XML Files etc)
- BOTO – Amazon S3
- REQUESTS/ BEAUTIFULUSOUP4/ LXML/ SELENIUM/ SCRAPPY – Web Crawler/ Parsing
- PDFMiner/ PyPDF2/ PDFMiner/ TEXTRACT – Extracting text from PDF’s/Images
Popular API’s:
- Pymongo – Managing MONGODB
- Pyspark – Managing SPARK
- Hadoopy, Pydoop – Managing HADOOP
- mrjob – Creating MAP REDUCE JOBS
- PyArrow – Managing APACHE ARROW
B. Python-based frameworks like Apache Airflow and LUIGI can be used for creating & managing Data Pipelines for ETL JOBS. If any other language is more suited for a certain task, AIRFLOW has the option to call external scripts as part of automation being done with Python.
Example, If we are use R for performing certain analysis, can call R scripts from Python workflow. Python is very powerful for combining different pieces together.
C. Pandas, NumPy, scipy, re, DateTime, string are python packages that can be used for data munging tasks (Clean, transform etc) and data analysis tasks
D. Pandas, matplotlib, seaborn, dash, bokeh are python packages that can be used for data visualization related tasks
E. NLTK, Spacy, gensim, textblob, re, string are packages can be used for mining & processing text data.
F. Opencv, scikit-image, mahotas, scipy, pillow, simpletk are some of the widely used packages for image/video data processing
G. Flask/Django frame works can be used for setting up API’s to surface the models or Data
H. fab or boto python packages can be used for automate AWS management or doing tasks across clusters.
You may also like to read:
1. 16 Best Big Data Analytics Tools And Their Key Features
2. What is Big Data Architecture, Its Types, Tools, and More?
3. Big Data Job Opportunities and Trends | Career in Big Data
For extensive learning in the field of Data Science and Big Data Analytics, you might explore CERTIFIED BIG DATA COURSE – Full Stack Data Engineering & Big Data Analytics course