Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Apache Spark Pitfalls: The Limitations of the Big Data Processing Giant
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Apache Spark Pitfalls: The Limitations of the Big Data Processing Giant
Big DataData ManagementExclusiveNewsSoftware

Apache Spark Pitfalls: The Limitations of the Big Data Processing Giant

Joseph Macwan
Joseph Macwan
5 Min Read
Apache Spark
SHARE

Apache Spark is a lightning fast solution to handle big data, process humongous data, and derive knowledge from it at record speed. The efficiency that is possible through Apache Spark make it a preferred choice among data scientists and big data enthusiasts.

Contents
The absence of an in-house file management systemA large number of small filesNear real-time processingNo automatic optimization processBack pressuresExpensive in-memory operationsPython useUnfathomable errors

But, alongside the many advantages and features of Apache Spark that make it appealing, there are some ugly aspects of the technology, too. We have listed some of the challenges that developers face when working on big data with Apache Spark.

Here are some aspects to flip side of Apache Spark so that you can make an informed decision whether or not the platform is ideal for your next big data project.

The absence of an in-house file management system

Apache Spark depends on some other third-party system for its file management capabilities, therefore making it less efficient than other platforms. When it is not merged with the Hadoop Distributed File System (HDFS), it needs to be used with another cloud-based data platform. This is considered as one of its key disadvantages.

More Read

big data helping page speed
Big Data Solves Website Loading Issues For Foreign Traffic
7 Accounting Practice Management Software that Rely on AI
The Financial Times New Search Engine
Are Data Scientists the Next Masters of the Universe?
Big Data and the Demise of Analog Retail

A large number of small files

This is another file management aspect that Spark is to be blamed for. When Apache Spark is used along with Hadoop, which it usually is, developers come across issues of small files. HDFS supports a limited number of large files, instead of a large number of small files.

Near real-time processing

When talking about Spark Streaming, the arriving stream is divided into batches of pre-defined intervals and each batch is then processed as a Resilient Distributed Dataset (RDD). After the operations are applied to each batch, the results are returned back in batches. Thus, this treating of data in batches does not qualify to be called a real-time processing, but since the operations are fast, Apache Spark can be called a near real-time data processing platform.

No automatic optimization process

Apache Spark does not have an automatic code optimization process in place, and thus there is a need to optimize the code manually. This comes as a disadvantage of the platform when most technologies and platforms are moving toward automation.

Back pressures

Back pressure is the condition when the data buffer fills completely, and there is a lining up of data at the input and the output channel. When this happens, no data is received or transferred until the buffer is emptied. Apache Spark does not have the required capability to handle this build-up of data implicitly, and thus this needs to be taken care of manually.

Expensive in-memory operations

In places where cost-effectiveness of processing is desirable, an in-memory processing capability can become a bottleneck as memory consumption is high and not handled from the perspective of the user. Apache Spark consumes and fills a lot of RAM to run its processes and analytics, thus being a costly approach to computing.

Python use

Developers and enthusiasts almost always recommend using Scala for working with Apache Spark, the reason being that each Spark release brings a thing or two for Scala and Java and updates the Python APIs to include newer things. Python users and developers are always a step behind Scala or Java users when working with Apache Spark. Also, with a pure RDD approach, Python is almost always slower than its Scala or Java counterpart.

Unfathomable errors

Developers complain of out-of-place errors when working with Apache Spark. Some failures are so vague that developers can spend hours simply looking at them and trying to defer what they mean.

With these lagging points, Apache Spark implementation may or may not be your way to go. Research is key in finding the right lightning fast big dats processing platform.

TAGGED:Apache Sparkbig databig data processingpython
Share This Article
Facebook Pinterest LinkedIn
Share
ByJoseph Macwan
Follow:
Joseph Macwan - A Technical Writer, working with Aegis Software, where he leads team to covers a wide range of topics like. He has been working on technical content for 9+ years, acquiring and developing content in areas such as software, IoT, ASP.NET, Dynamics 365 Services, Microsoft dynamics 365 crm.

Follow us on Facebook

Latest News

sales and data analytics
How Data Analytics Improves Lead Management and Sales Results
Analytics Big Data Exclusive
ai in marketing
How AI and Smart Platforms Improve Email Marketing
Artificial Intelligence Exclusive Marketing
AI Document Verification for Legal Firms: Importance & Top Tools
AI Document Verification for Legal Firms: Importance & Top Tools
Artificial Intelligence Exclusive
AI supply chain
AI Tools Are Strengthening Global Supply Chains
Artificial Intelligence Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

using big data for clean energy
Big DataExclusive

5 Ways Business Data Is Changing How People View Green Energy

9 Min Read
how big data changing internet experience
Big Data

How Big Data is Changing the Internet Experience for Average Consumers

5 Min Read
Linux device drivers data
Big DataExclusive

Key Tips to Writing Linux Device Drivers for Big Data Environments

6 Min Read
use data to increase customer engagement
Business IntelligenceData CollectionData ManagementWeb Analytics

How To Use Data To Increase Customer Engagement On Your Website

7 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?