Apache Spark is an open-source distributed cluster-computing engine designed to process big data workloads faster in parallel or batch modes. Spark is written in the Scala language and is based on Hadoop’s MapReduce structure. Perhaps the greatest advantage Spark delivers is in-memory caching, which enhances its processing speed and optimized query execution. This eliminates the process of writing back to disc, as is common with previous technologies. This means that data can be processed and also run on the Spark platform. Spark is a versatile framework that provides development APIs in Python, Java, R, and Scala languages and supports code reusability. As mentioned above, Spark can be used for parallel processing, batch processing, real-time analytics, interactive SQL queries, graph processing, and machine learning.
Apache Spark consists of the following components
- Spark Core Engine is the general engine on which other features are built, and it is compatible with the other five components. The Spark Core engine is the component that offers in-memory caching, which enables distributed processing.
- Spark SQL module, which carries out structured SQL data processing, including on big data workloads
- MLib distributed framework for scalable machine learning using Python, Scala, Java, and R APIs.
- Spark Streaming enables analytics for real-time data from multiple sources by transforming it into Resilient Distributed Datasets (RDD).
- GraphX distributed framework for carrying out graph analysis using libraries like PySpark Core and PySparkSQL
- Spark R API for running R language on Spark.
PySpark and Databricks are two popular frameworks designed for processing large workloads. Are you undecided about whether to pursue a PySpark Certification or Databricks certification? This article will help you make an informed decision.
What is PySpark?
PySpark is an API provided by Apache Spark during installation that enables one to write Spark applications using Python language and perform scaled distributed analysis of Resilient Distributed Datasets (RDDs). PySpark allows you to read data from a range of file formats, including JSON, CSV, Parquet, and various databases.
PySpark can be installed and run on self-hosted environments like virtual machines and computers or on cloud environments like AWS, AZURE, and Google Cloud platforms. It has few libraries like the popular Py4j library that enables a Python interface with JVM objects. It is also compatible with external libraries like PySparkSQL for executing SQL-like analysis and queries on large structured and semistructured datasets, GraphFrames library for graph analysis using PySpark Core, and PySparkSQL, and MLib library for machine learning processing.
Advantages of PySpark
PySpark is a Python API. Thus, it leverages the benefits of Python, a simplified syntax with a vast number of libraries that is easy to learn, and of Spark, a fast and efficient computing technology to process big data.
PySPark comes with several other advantages. These are:
- PySpark enables fast processing of large datasets in memory as it eliminates reading and writing to disc
- Spark has more than 80 high-level operators, which makes it possible to develop parallel applications. Thus, it is possible to run workloads in parallel in a distributed cluster with PySpark.
- PySpark excels at real-time analytics of streaming data.
- Apache Spark framework is known for its fault-tolerance property using RDD abstraction to enable it to self-recover from any fault/failure of a node or cluster without data loss.
What is Databricks?
Databricks is a cloud-based analytics platform developed by the creators of Apache Spark. This framework provides a fast means of setting up clusters, exploring, and modeling big data. Databricks is a high-performing alternative for MapReduce that processes, transforms, and explore big data using machine learning models on cloud platforms. This allows organizations to build machine learning models and leverage the in-built data visualization functions in Databricks for more effective analytics.
Databricks framework is available on major cloud platforms like AWS, Azure, and Google Cloud. Databricks is also fast because it runs on distributed systems allowing not only efficient fail-proof processing but also scaling up and down on demand. Simply put, Databricks is a web platform that offers cluster management for Apache Spark workloads.
Advantages of Databricks
- Databricks leverages the LakeHouse architecture and is thus a one-stop and interactive analytics platform for data warehousing, analysis, and other data requirements. This provides data scientists and engineers with a single source of data for simplified analytics.
- Databricks provides machine learning data modeling capabilities and is compatible with cloud platforms like AWS, Google Cloud, and Microsoft Azure which makes it possible for organizations to manage large volumes of data effectively.
- Databricks is compatible with SparkSQL for SQL querying, PySpark for distributed analytics using Python language, SparkR for running R on Spark, SparkML for building predictive models, and others like PowerBI and Tableau.
- Databricks supports several popular coding languages, including Python, R, Scala, and SQL.
Should I go for Databricks or PySpark?
Whether to opt for Pyspark or Databricks will depend on your workload requirements.
Apache PySpark is an API for Python language that is designed for processing large volumes of data efficiently. PySpark offers distributed and in-memory processing which makes it a good option if you need parallel processing. It can be installed and run on self-hosted or cloud environments. For this reason.
PySpark is good for you if:
- You are familiar with Python and Spark because learning Spark will give you the added advantage when it comes to developing scalable pipelines and analytics for scalable workloads.
- You want to process machine learning and visualization workloads because PySpark comes with machine learning and graph modules to deliver extended functionality.
- Your workload will benefit from both batch and streaming data analytics.
- You want to run workloads on the cloud and will benefit from fast distributed in-memory processing.
On the other hand, PySpark offers such high-level abstraction and has a steep learning curve, particularly for beginners. Also, using PySpark limits you from working with the internal functions of Spark since Spark is written in Scala, and PySpark leverages Python language.
Databricks, on the other hand, leverages distributed cloud computing to process workloads. It is compatible with cloud platforms like AWS, GCP, and Microsoft Azure.
Databricks is good for you if:
- You need a framework that is compatible with multiple languages. Databricks framework is compatible with Python, R, Scala, and SQL, which are converted to interact with Spark in the backend using APIs. It is therefore not necessary for users to learn other languages.
- You need an interactive platform that fosters collaboration. This is usually important for a team of data scientists or engineers who are collaborating on projects like machine learning or model creation.
- You need a framework with built-in version control functionality and data visualization tools. These facilitate application innovation, development, and monitoring with enhanced security.
- You need a framework that is good for both large scalable workloads like big data analytics and machine learning modeling as well as smaller workloads like application development and testing.
- You need a highly available framework that is optimized for cloud environments
Both PySpark and Databrick are optimized for processing large scalable workloads and are compatible with the cloud. The difference is that PySpark leverages Python language while Databricks framework leverages cloud and machine learning capabilities. Both PySpark and Databrick are built to work in distributed environments and are thus fault-tolerant.