Apache Spark touts itself as a simple, fast, scalable, and unified solution. How can it benefit your business, though, specifically when it comes to business intelligence issues like data analysis and machine learning?
In this blog, you’ll learn more about what makes Apache Spark unique, its pros and cons, and how you can use it for business intelligence, so you can decide if it’s a good fit for you and your team.
What Is Apache Spark?
Apache Spark is a multi-language engine used for data engineering, data science, and machine learning on single-node machines and clusters.
It’s currently the world’s most widely used scalable computing engine.
Thousands of organizations, including 80 percent of Fortune 500 companies like Netflix and Amazon, rely on Apache Spark. Over 2,000 contributors from industry and academia have also participated in this open-source project.
Key Features of Apache Spark
Apache Spark stands out from other tools with the following features and uses:
With Apache Spark, you can unify data processing in batches or real-time streaming with the following languages: Python, SQL, Scala, Java, and R.
Users can trust Apache Spark to provide distributed ANSI SQL queries. It assists with dashboarding, reporting, and other essential processes and is more efficient than many data warehouses!
Data Science at Scale
Apache Spark users can avoid downsampling and perform Exploratory Data Analysis (EDA) on a petabyte scale.
With Apache Spark, you can train machine learning algorithms on a laptop, then use the same code on fault-tolerant clusters (including those that consist of thousands of machines).
Apache Spark Ecosystem
Apache Spark integrates with numerous frameworks and helps them scale to thousands of machines simultaneously. The following are some of the most significant integrations Spark users can enjoy:
- Apache Superset
- Apache Kafka
- Delta Lake
- Apache Airflow
- Microsoft SQL Server
- Apache Orc
Spark users also gain access to a thriving open-source community. Contributors from across the globe build features, create documentation, and assist other users in helping them get the most out of their experience.
Apache Spark Pros
It’s not hard to understand why so many organizations use and love Apache Spark. Here are some of the pros users mentioned repeatedly:
One of Apache Spark’s greatest strengths is its speed. Data scientists appreciate that it handles large-scale processing 100 times faster than Apache Hadoop.
Spark’s speed comes from its in-memory RAM computing system (compared to Hadoop’s local memory space for data storage).
Ease of Use
Spark is also known for its ease of use. It carries convenient, user-friendly APIs and over 80 high-level operators for building parallel apps.
Spark supports ‘MAP,’ ‘reduce,’ machine learning, graph algorithms, SQL queries, streaming data, and more. These advanced analytics make it a versatile option for many different users.
Apache Spark is a highly dynamic solution, especially for those looking to develop parallel applications, thanks to its 80-plus high-level operators.
Supports Multiple Coding Languages
Apache Spark supports multiple coding languages, including Python, Java, and Scala. Regardless of your preferred coding language, you can use it with Spark.
Increased Big Data Access
Apache Spark has created (and continues to generate) numerous opportunities for big data processing. That’s why leaders at IBM chose to educate over 1 million data engineers and data scientists on Spark.
Many Spark users also love its open-source nature and the community attached to it. If you have questions or want to learn more about the inner workings of Spark, you’ll have no trouble finding the information or support you need.
Apache Spark Cons
Despite all the benefits Spark offers, there are also some downsides potential users should keep in mind. Consider these cons before deciding to move forward with Spark:
Requires Manual Code Optimization
Spark doesn’t offer any options for automatic code optimization. Users have to manually update and optimize their code, which some may find frustrating or inconvenient.
No File Management System
Spark doesn’t include its file management system. Users have to rely on other platforms, such as Hadoop and its Distributed File System (HDFS) or another cloud-based tool.
One of the most common complaints about Spark is that its machine learning feature doesn’t offer as many algorithms as other tools. If you’re looking for a solution with more readily available algorithms, Spark probably is not the best choice.
Small Files Issues
Apache Spark is also known for its challenges with small files. When using Spark alongside Hadoop, developers find that Hadoop’s Distributed File System provides a limited number of large files rather than a large number of small files, which isn’t ideal for some users’ needs and preferences.
Apache doesn’t support record-based window criteria. It only offers time-based window criteria, which may be frustrating or inconvenient for some users.
Not Suitable for Multi-User Environments
Spark is not a good fit for a multi-user environment since it cannot handle multiple users concurrently.
How Can Apache Spark Be Used for Business Intelligence?
Many business intelligence professionals trust Apache Spark for their everyday needs and processes. The following are some specific ways Spark works in the BI world:
Many organizations like Spark because of its streaming features.
With Spark Streaming, the code used for batch processing can also be used for real-time computations (with a few minor adjustments), increasing programmer productivity.
Some businesses also rely on Spark Streaming to detect patterns and anomalies.
Spark’s analytics are also highly attractive to BI professionals.
Its advanced features can assist with numerous real-world problems, such as online advertising and marketing, fraud detection, research challenges, and more. Users can also use Spark to develop graph and machine learning analytics libraries.
Flexibility and Multiple User Cases
Spark is also popular among businesses of various sizes and in numerous industries because of its flexibility and versatility.
Spark is a staple in many Big Data infrastructure stacks because it assists with data ingestion, storage, processing, and analytics. It covers all the bases and allows for more streamlined processes.
How Can Yurbi Help in Apache Spark?
Unfortunately, Yurbi has no native integration for Apache Spark. However, you can still integrate it through third-party ODBC drivers that enable communication with it similar to a relational database. CDATA and Progress are some examples of such drivers.
Furthermore, Yurbi offers the capability of merging data from Apache Spark with information from other data sources to create comprehensive reports and dashboards. It also allows users to retrieve and analyze data without the need for direct access or advanced query writing skills.
Yurbi provides a powerful presentation layer BI tool as part of your tech stack. Ad-hoc querying, data blending, data visualization, modern business intelligence, and embedded analytics.
And a key use case of Yurbi is to provide white label, embedded analytics with multi-tenant security for your SaaS or on-premise software.
You might think that BI tools like these cost a fortune, but Yurbi knows that everyone deserves top-level quality services, so it offers pricing points that are perfect for small and medium-sized entrepreneurs.