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Building Real-time Data Pipeline using Apache Spark

Real-time analytics has become mission-critical for organizations looking to make data-driven business decisions.

Whether it is the Internet of things & Anomaly Detection (sensors sending real-time data), high-frequency trading (real-time bidding), social networks (real-time activity), server/traffic monitoring, providing real-time reporting brings in tremendous value.

However, to process large amounts of real-time or streaming data requires you to build a data processing pipeline.

The main challenge in building such a pipeline is to minimize latency & achieve near real-time processing rate to process high-throughput data.

This blog explores how you can create a scalable, reliable and fault-tolerant data pipeline capable of fetching event-based data and streaming those events to Apache Spark, all of which will be done near real-time.

Before we get into building such a system, let us understand what is a data pipeline & what are the several components of the spark data pipeline architecture.

What is a Data Pipeline?

A data pipeline is a software that consolidates data from multiple sources and makes it available to be used strategically.

The data pipeline architecture consists of several layers:-

1) Data Ingestion
2) Data Collector
3) Data Processing
4) Data Storage
5) Data Query
6) Data Visualization

Let’s get into details of each layer & understand how we can build a real-time data pipeline.

1) Data Ingestion

Data ingestion is the first step in building a data pipeline. Data Ingestion helps you to bring data into the pipeline.

It means taking unstructured data from where it is originated into a data processing system where it can be stored & analyzed for making data-driven business decisions.

Data Ingestion pipeline process to be effective needs, to begin with prioritizing data sources, validating individual files & routing data streams to the correct destination.

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It should be well designed to handle and upgrade the new data sources, technology and applications. It should also allow rapid consumption of data.

You can use Open source Data Ingestion Tools like Apache Flume. Apache Flume is a reliable distributed service for efficiently collecting, aggregating, and moving large amounts of log data.

Its functions are –

  1. Stream the Data — Ingest streaming data from multiple sources into Hadoop for storage and analysis.
  2. Insulate the System — Buffer storage platform from transitory spikes, when the rate of incoming data surpasses the rate at which data is written to the destination.
  3. Scale Horizontally — Ingest new data streams & additional volume as needed.

You can also use Apache NiFi or elastic Logstash. All of them can ingest Data of all Shapes, Sizes, and Sources.

2) Data Collector

In Data collector layer, the focus is on the transportation of data from ingestion layer to rest of data pipeline architecture.

We use a messaging system called Apache Kafka to act as a mediator between all the programs that can send and receive messages.

Apache Kafka can process streams of data in real-time and store streams of data safely in a distributed replicated cluster.

Kafka works along with Apache Storm, Apache HBase and Apache Spark for real-time analysis and rendering of streaming data.

There are four components involved in moving the data in and out of Apache Kafka –

  1. Topics — Topic is a user-defined category to which messages are published.
  2. Producers — Producers report messages to one or more topics
  3. Consumers — Consumers subscribe to topics & process the reported messages.
  4. Brokers — Brokers manage the persistence & replication of message data.

3) Data Processing

In this layer, the main focus is to process the collected data from the previous layer. Layer helps to route the data to a different destination, classify the data flow, and it’s the first point where the analytics takes place.

You can use Apache Spark for the real-time data processing as it is a fast, in-memory data processing engine.

It can run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. Spark offers over 80 high-level operators that make it easy to build parallel apps.

And you can use it interactively from the Scala, Python and R shells. Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3.

It’s elegant and expressive development APIs to allow data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets.

You can use other platforms like Apache Storm and Apache Flink depending on your particular use case.

4) Data Storage

This layer ensures to keep data in the right place based on usage.

A Relational Database is a place you may have stored our data over the years, but with the new big data enterprise applications, you should no longer assume that your persistence should be relational.

You need different databases to handle the different variety of data, but using different databases creates overhead issues.

You can use Polyglot persistence to use multiple databases to power a single application. Advantages of Polygon Persistence are faster response times, it helps your data to scale well and gives you a rich experience.

Tools used for data storage can include HDFS, GFS, and Amazon S3.

5) Data Query

This layer is where strong analytic processing takes place. Analytics Query Tools available are Apache Hive, Spark SQL, Amazon Redshift, Presto.

Apache Hive is data warehouse built on top of Apache Hadoop for providing data summarization, ad-hoc query, and analysis of large datasets.

Data analysts use Hive to query, summarize, explore and analyze the data, then turn it into actionable business insight.

Apache Hive helps to project structure onto the data in Hadoop and to query that data using a SQL.

You can also use Spark SQL for data query. Spark SQL is a Spark module for structured data processing.

Presto is an open-source distributed SQL query engine used to run interactive analytic queries against data sources of all sizes.

6) Data Visualization

This layer focus on Big Data Visualization.

You need something that grabs people’s attention, pull them in & make your findings well-understood. Data Visualization layer provides full Business Infographics.

Based on your business requirements, you can create Custom dashboards, Real-Time Dashboards using data visualization tools in the market.

Tableau is one of the best data visualization tools available in the market today with a Drag and Drop functionality.

Tableau allows the users to design Charts, Maps, Tabular, Matrix reports, Stories and Dashboards without having any technical knowledge.

It helps you to quickly analyze, visualize and share information whether it’s structured or unstructured, petabytes or terabytes has millions or billions of rows, you can turn big data into big ideas.

You can use Kibana dashboard. A Kibana dashboard displays a collection of pre-saved visualizations. You can arrange and resize the visualizations as need and save dashboards, and they can be reloaded and shared.

You can use Intelligent agents, Angular.js, React.js & Recommender systems as well for Data Visualization.


Building a data pipeline is a long & tedious process, and you require lots of technical expertise & experience to create one layer by layer.

Your Data science needs to focus on creating ML Models & making use of the resourceful Data coming out of the data pipeline, without worrying about infrastructure, scaling, data integration, security etc.

We have worked on various projects building data pipeline for startups & enterprise clients.

Are you planning to build one? Then hire us!

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