A Data Pipeline is a system for transporting data from one location (the source) to another (the destination) (such as a data warehouse). Data is transformed and optimized along the way, and it eventually reaches a state that can be analyzed and used to develop business insights.
Apache Spark is a large-scale data processing open-source unified analytics engine. Apache Spark is a programming interface for clusters that includes implicit data parallelism and fault tolerance. The Apache Spark codebase was originally developed at the University of California, Berkeley’s AMPLab and later donated to the Apache Software Foundation, which has since maintained it.
This blog talks about how to build a scalable, reliable, and fault-tolerant Apache Spark Data Pipeline that can fetch event-based data and stream it in near real-time.
Table Of Contents
- What is Data Pipeline?
- What is Apache Spark?
- How to Build Apache Spark Data Pipeline?
What is Data Pipeline?
A Data Pipeline is a series of steps that ingest raw data from various sources and transport it to a storage and analysis location. The data is ingested at the start of the pipeline if it has not yet been loaded into the data platform. Then there’s a series of steps, each of which produces an output that becomes the input for the next step. This will go on until the pipeline is finished. Independent steps may be run in parallel in some cases.
Organizations are moving data between more and more applications as they look to build applications with small code bases that serve a very specific purpose (these types of applications are called “microservices“). This makes the efficiency of Data Pipelines a critical consideration in their planning and development. Data generated by a single source system or application may feed multiple Data Pipelines, and those pipelines may be dependent on the outputs of multiple other pipelines or applications.
Consider a single social media comment. This event could provide information for a real-time report that counts social media mentions, a sentiment analysis app. This returns a positive, negative, or neutral result, or an app that plots each mention on a world map.
Even though all of the data comes from the same source, each of these applications is built on its own set of Data Pipelines that must run smoothly before the end-user sees the result.
Data Transformation, Augmentation, Enrichment, Filtering, Grouping, Aggregation, and the application of algorithms to that data are all common steps in Data Pipelines.
Filtering and features that provide resiliency against failure may also be included in a pipeline.
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What is Apache Spark?
Apache Spark began as a research project at UC Berkley’s AMPLab, a collaboration of students, researchers, and faculty focusing on data-intensive application domains, in 2009. Apache Spark’s goal was to create a new framework that was optimized for fast iterative processing like Machine Learning and interactive Data Analysis while retaining Hadoop MapReduce’s scalability and fault tolerance.
Apache Spark was open-sourced under a BSD license after the first paper, “Spark: Cluster Computing with Working Sets,” was published in June 2010. In June 2013, Apache Spark was accepted into the Apache Software Foundation’s (ASF) incubation program, and in February 2014, it was named an Apache Top-Level Project. Apache Spark can run standalone, on Apache Mesos, or on Apache Hadoop, which is the most common configuration.
Apache Spark is a distributed processing system for big data workloads that is open-source and free to use. For quick analytic queries against any size of data, it uses in-memory caching and optimized query execution. It supports code reuse across multiple workloads, including Batch Processing, Interactive Queries, Real-Time Analytics, Machine Learning, and Graph Processing, and it provides development APIs in Java, Scala, Python, and R. FINRA, Yelp, Zillow, DataXu, Urban Institute, and CrowdStrike are just a few examples of companies that use it. With 365,000 meetup members in 2017, Apache Spark is one of the most popular big data distributed processing frameworks.
Apache Spark is now one of the most popular projects in the Hadoop ecosystem, with many companies using it in conjunction with Hadoop to process large amounts of data. Apache Spark had 365,000 meetup members in 2017, a 5x increase in just two years. Since 2009, it has benefited from the contributions of over 1,000 developers from over 200 organizations.
Hadoop MapReduce is a parallel, distributed algorithm for processing large data sets. Without having to worry about work distribution or fault tolerance, developers can write massively parallelized operators. However, MapReduce struggles with the sequential multi-step process required to run a job. MapReduce reads data from the cluster, runs operations on it, and writes the results back to HDFS at the end of each step. MapReduce jobs are slower due to the latency of disc I/O because each step necessitates a disc read and write.
By performing processing in memory, reducing the number of steps in a job, and reusing data across multiple parallel operations, Apache Spark was created to address the limitations of MapReduce. With Spark, data is read into memory in a single step, operations are performed, and the results are written back, resulting in significantly faster execution.
Apache Spark also reuses data by using an in-memory cache to greatly accelerate machine learning algorithms that call the same function on the same dataset multiple times. The creation of DataFrames, an abstraction over the Resilient Distributed Dataset (RDD), which is a collection of objects cached in memory and reused in multiple Apache Spark operations, allows for data reuse. Apache Spark is now many times faster than MapReduce, especially when performing machine learning and interactive analytics.
Key Features of Apache Spark
Apache Spark provides the following rich features to ensure a hassle-free Data Analytics experience:
- High Processing Capabilities: Apache Spark leverages Resilient Distributed Datasets (RDDs) to minimize the I/O operations as compared to its peer MapReduce. Moreover, it offers 100 times faster memory performance, and on disk, it operates with 10 times faster speed.
- Easy Usage: Apache Spark allows you to work with numerous programming languages. Moreover, it offers 80 operators to simplify your development tasks. Apache Spark’s user interface is simple to understand and even allows you to reuse the code for critical tasks like manipulating historical data, running ad-hoc queries, etc.
- Fault Tolerance: RDDs allow Apache Spark to manage situations of node failure and safeguard your cluster from data loss. Moreover, it regularly stores the transformations and actions, empowering you to restart from the last checkpoint.
- Real-Time Processing: Traditional tools like MapReduce allow for processing data only if available in Hadoop Clusters. Apache Spark, on the other hand, uses multiple language-integrated robust APIs to support data processing in real-time.
To learn more about Apache Spark, visit here.
Key Benefits of Apache Spark
- Fast: Apache Spark can run fast analytic queries against data of any size thanks to in-memory caching and optimized query execution.
- Developer-Friendly: Apache Spark comes with native support for Java, Scala, R, and Python, giving you a wide range of languages to choose from when developing your applications. These APIs make it simple for your developers by hiding the complexity of distributed processing behind simple, high-level operators, resulting in a significant reduction in the amount of code required.
- Multiple Workloads: Apache Spark can handle a variety of tasks, such as interactive queries, real-time analytics, machine learning, and graph processing. Multiple workloads can be seamlessly combined in one application.
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How to Build Apache Spark Data Pipeline?
A Data Pipeline is a piece of software that collects data from various sources and organizes it so that it can be used strategically.
An Apache Spark Data Pipeline consists of building the following layers:
- Apache Spark Data Pipeline: Data Ingestion
- Apache Spark Data Pipeline: Data Collector
- Apache Spark Data Pipeline: Data Processing
- Apache Spark Data Pipeline: Data Storage
- Apache Spark Data Pipeline: Data Query
- Apache Spark Data Pipeline: Data Visualization
Apache Spark Data Pipeline: Data Ingestion
The first step in constructing a Data Pipeline is to collect data. Data Ingestion is a tool that allows you to load data into your pipeline. It entails transferring unstructured data from its source to a data processing system, where it can be stored and analyzed to aid in the making of data-driven business decisions.
To be effective, the Data Ingestion process must start with prioritizing data sources, validating individual files, and routing data streams to the correct destination. It must be well-designed to accommodate and upgrade new data sources, technology, and applications. It should also allow for rapid data consumption.
You can use Apache Flume, an open-source data ingestion tool. Apache Flume is a dependable distributed service for collecting, aggregating, and moving large amounts of log data efficiently.
Its functions are as follows:
- Stream the Data: Import lives streaming data from a variety of sources into Hadoop for storage and analysis.
- Insulate the System: When the rate of incoming data exceeds the rate at which data is written to the destination, buffer the storage platform.
- Scale Horizontally: As needed, add new data streams and volume to the system.
Apache NiFi or Elastic Logstash are two other options. They can all take in data of any shape, size, or source.
Apache Spark Data Pipeline: Data Collector
The transport of data from the ingestion layer to the rest of the data pipeline is the focus of the Data Collector layer. To act as a mediator between all the programs that can send and receive messages, you can use Apache Kafka, which is a messaging system.
Apache Kafka can process and store data streams in a distributed replicated cluster in real-time.
For real-time analysis and rendering of streaming data, Kafka collaborates with Apache Storm, Apache HBase, and Apache Spark.
Moving data into and out of Apache Kafka involves four different components:
- Topics: A topic is a user-defined category where messages are posted.
- Producers: Messages are reported to one or more topics by producers.
- Consumers: Consumers subscribe to topics and process the messages that are sent to them.
- Brokers: Brokers are in charge of message data persistence and replication.
Apache Spark Data Pipeline: Data Processing
The main goal of this layer is to process the data collected in the previous layer. Layers assist in routing data to different destinations, classifying data flow, and acting as the first point of analytics.
Apache Spark is a fast, in-memory data processing engine that can be used for real-time data processing. It can run programs 100 times faster in memory or 10 times faster on disc than Hadoop MapReduce. Over 80 high-level operators are available in Spark, making it simple to create parallel apps. It’s also interactively usable from the Scala, Python, and R shells. Apache Spark can be used with Hadoop, Mesos, on its own, or in the cloud.
HDFS, Cassandra, HBase, and S3 are just a few of the data sources it can access.
Its elegant and expressive development APIs enable data workers to run streaming, machine learning, or SQL workloads that require fast iterative access to datasets efficiently.
Depending on your needs, you can use other platforms such as Apache Storm or Apache Flink.
ApacheSpark Data Pipeline: Data Storage
This layer ensures that data is kept in the correct location based on usage. You may have stored your data in a relational database over time, but with the new big data enterprise applications, you should no longer assume that your persistence should be relational.
Different databases are required to handle the various types of data, but using multiple databases creates overhead issues.
You can use Polyglot persistence to power a single application with multiple databases. Polyglot Persistence has several advantages, including faster response times, data scaling, and a rich user experience.
HDFS, GFS, and Amazon S3 are examples of Data Storage tools.
Apache Spark Data Pipeline: Data Query
Strong analytic processing takes place in this layer. Apache Hive, Spark SQL, Amazon Redshift, and Presto are some of the analytics query tools available.
Apache Hive is a Data Warehouse for data summarization, ad-hoc querying, and analysis of large datasets built on top of Apache Hadoop. Apache Hive is used by data analysts to query, summarise, explore, and analyze data before turning it into actionable business insight.
Apache Hive assists in projecting structure onto Hadoop data and querying that data using SQL.
For data querying, you can use Spark SQL. Spark SQL is a structured data processing module for the Spark programming language.
Presto is an open-source distributed SQL query engine that can be used to run interactive analytic queries against a wide range of data sources.
Apache Spark Data Pipeline: Data Visualization
This layer is dedicated to the visualization of large amounts of data. You’ll need something that will grab people’s attention, draw them in, and help them understand your findings. Full Business Infographics are provided by the Data Visualization layer.
Using Data Visualization tools on the market, you can create Custom dashboards and Real-Time Dashboards based on your business requirements.
Tableau is a drag-and-drop data visualization tool that is one of the best available on the market today. Without any technical knowledge, Tableau users can create Charts, Maps, Tabular, Matrix reports, Stories, and Dashboards. You can turn big data into big ideas by quickly analyzing, visualizing, and sharing information, whether it’s structured or unstructured, petabytes or terabytes in size, with millions or billions of rows.
Kibana Dashboard can also be used as it shows a collection of visualizations that have already been saved. You can rearrange and resize the visualizations as needed, as well as save and share dashboards.
For Data Visualization, you can use intelligent agents, Angular.js, React.js, and recommender systems.
This article explains how to build Apache Spark Data Pipeline with different layers. It also describes Apache Spark and its features.
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