TrustRadius Insights
Apache Spark is an incredibly versatile tool that has been widely adopted across various departments for processing very large datasets …
Overview
Recent Reviews
Reviewer Pros & Cons
Assistant Professor
The National Institute of Engineering, Mysuru (Education Management, 501-1000 employees)
Product Demos
Spark Project | Spark Tutorial | Online Spark Training | Intellipaat
YouTube
Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginners | Simplilearn
YouTube
Apache Spark Full Course | Apache Spark Tutorial For Beginners | Learn Spark In 7 Hours |Simplilearn
YouTube
Apache Spark Architecture | Spark Cluster Architecture Explained | Spark Training | Edureka
YouTube
Introduction to Databricks [New demo linked in description]
YouTube
Apache Spark Tutorial | Spark Tutorial for Beginners | Spark Big Data | Intellipaat
YouTube
Product Details
- About
- Tech Details
What is Apache Spark?
Apache Spark Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Comparisons
Compare with
Reviews and Ratings
(159)
Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
- Pros
- Cons
- Recommendations
Apache Spark is an incredibly versatile tool that has been widely adopted across various departments for processing very large datasets and generating summary statistics. Users have found it particularly useful for creating simple graphics when working with big data, making it a valuable asset for analytics departments. It is also used extensively in the banking industry to calculate risk-weighted assets on a daily and monthly basis for different positions. The integration of Apache Spark with Scala and Apache Spark clusters enables users to load and process large volumes of data, implementing complex formulas and algorithms. Additionally, Apache Spark is often utilized alongside Kafka and Spark Streams to extract data from Kafka queues into HDFS environments, allowing for streamlined data analysis and processing.
One of the key strengths of Apache Spark lies in its ability to handle large volumes of retail and eCommerce data, providing cost and performance benefits over traditional RDBMS solutions. This makes it a preferred choice for companies in these industries. Furthermore, Apache Spark plays a crucial role in supporting data-driven decision-making by digital data teams. Its capabilities allow these teams to build data products, source data from different systems, process and transform it, and store it in data lakes.
Apache Spark is highly regarded for its ability to perform data cleansing and transformation before inserting it into the final target layer in data warehouses. This makes it a vital tool for ensuring the accuracy and reliability of data. Its faster data processing capabilities compared to Hadoop MapReduce have made Apache Spark a go-to choice for tasks such as machine learning, analytics, batch processing, data ingestion, and report development. Moreover, educational institutions rely on Apache Spark to optimize scheduling by assigning classrooms based on student course enrollment and professor schedules.
Overall, Apache Spark proves itself as an indispensable product that meets the needs of various industries by offering efficient distributed data processing, advanced analytics capabilities, and seamless integration with other technologies. Its versatility allows it to support a wide range of use cases, making it an essential tool for anyone working with big data.
Great Computing Engine: Apache Spark is praised by many users for its capabilities in handling complex transformative logic and sophisticated data processing tasks. Several reviewers have mentioned that it is a great computing engine, indicating its effectiveness in solving intricate problems.
Valuable Insights and Analysis: Many reviewers find Apache Spark to be useful for understanding data and performing data analytical work. They appreciate the valuable insights and analysis capabilities provided by the software, suggesting that it helps them gain deeper understanding of their data.
Extensive Set of Libraries and APIs: The extensive set of libraries and APIs offered by Apache Spark has been highly appreciated by users. It provides a wide range of tools and functionalities to solve various day-to-day problems, making it a versatile choice for different data processing needs.
Challenging to Understand and Use: Some users have found Apache Spark to be challenging to understand and use for modeling big data. They struggle with the complexity of the software, leading to a high learning curve.
Lack of User-Friendliness: The software is considered not user-friendly, with a confusing user interface and graphics that are not of high quality. This has resulted in frustration among some users who find it difficult to navigate and work with.
Time-Consuming Processing: Apache Spark can be time-consuming when processing large data sets across multiple nodes. This has been reported by several users who have experienced delays in their data processing tasks, affecting overall efficiency.
When using Spark for big data tasks, users commonly recommend familiarizing yourself with the documentation and gaining experience. They emphasize investing time in reading and understanding the documentation to overcome any initial challenges. As users gain experience, they find working with Spark becomes easier and more efficient.
Users also suggest utilizing Spark specifically for true big data problems, where its capabilities and performance shine. They highlight that Spark is well-suited for tackling large-scale data processing tasks.
Additionally, users find value in leveraging the R and Python APIs in Spark. These APIs allow them to work with Spark using familiar programming languages such as R and Python, making it easier to analyze and process data.
Overall, users advise diving into the documentation, utilizing Spark for big data challenges, and leveraging the R and Python APIs to enhance their experience with Spark.
Attribute Ratings
Reviews
(1-4 of 4)Companies can't remove reviews or game the system. Here's why
November 08, 2020
Epic Computation Engine Framework
Score 9 out of 10
Vetted Review
Verified User
Apache Spark is being used by our organization for writing ETL applications. It enables us to ingest thousands of records of data to database tables.
- Great computing engine for solving complex transformative logic
- Useful for understanding data and doing data analytical work
- Gives us a great set of libraries and api to solve day-to-day problems
- High learning curve
- Complexity
- More documentation
- More developer support
- More educational videos
- Saves lot of time
- Very powerful
- Automates lots of manual work
- Higher depth of knowledge is required to understand and perform analysis
Spark is simply awesome to work on with any data sets and also has an in-memory database which makes it very flexible.
September 19, 2020
A powerhouse processing engine.
We use Apache Spark for cluster computing in large-scale data processing, ETL functions, machine learning, as well as for analytics. Its primarily used by the Data Engineering Department, in order to support the data lake infrastructure. It helps us to effectively manage the great amounts of data that come from our clusters, ensuring the capacity, scalability, and performance needed.
- Speed: Apache Spark has great performance for both streaming and batch data
- Easy to use: the object oriented operators make it easy and intuitive.
- Multiple language support
- Fault tolerance
- Cluster managment
- Supports DF, DS, and RDDs
- Hard to learn, documentation could be more in-depth.
- Due to it's in-memory processing, it can take a large consumption of memory.
- Poor data visualization, too basic.
- Saved time and resources for the company because of it's agility
- High performance data processing.
January 12, 2020
Apache Spark -- The best big data solution
Score 8 out of 10
Vetted Review
Verified User
We were working for one of our products, which has a requirement for developing an enterprise-level product catering to manage a vast amount of Big data involved. We wanted to use a technology that is faster than Hadoop and can process large scale data by providing a streamlined process for the data scientists. Apache Spark is a powerful unified solution as we thought to be.
The main problem that we identified in our existing approach was that it was taking a large amount of time to process the data, and also the statistical analysis of the data was not up to the mark. We wanted a sophisticated analytical solution that was easy and fast to use. With using Apache Spark, the processing was made 5 times faster than earlier, giving rise to pretty good analytics. With Spark, across a cluster of machines, the data abstraction was achieved by using RDDs.
The main problem that we identified in our existing approach was that it was taking a large amount of time to process the data, and also the statistical analysis of the data was not up to the mark. We wanted a sophisticated analytical solution that was easy and fast to use. With using Apache Spark, the processing was made 5 times faster than earlier, giving rise to pretty good analytics. With Spark, across a cluster of machines, the data abstraction was achieved by using RDDs.
- DataFrames, DataSets, and RDDs.
- Spark has in-built Machine Learning library which scales and integrates with existing tools.
- The data processing done by Spark comes at a price of memory blockages, as in-memory capabilities of processing can lead to large consumption of memory.
- The caching algorithm is not in-built in Spark. We need to manually set up the caching mechanism.
- The ROI was increased by considerable percentage after using Apache Spark.
- Apache Spark provided the agility towards supporting multiple applications.
- Hadoop and Amazon EMR (Elastic MapReduce)
1. Apache Spark is almost 100 % faster than Hadoop.
2. Apache Spark is more stable than Amazon EMR.
3. The end to end distributed machine library is more robust in Apache Spark.
4. For very large data sets, Apache Spark is more trustworthy than the other two.
5. For data transformations, Apache Spark provides a very rich set of APIs.
6. The interface provided for SQL in Apache Spark is easy to understand as compared to others.
2. Apache Spark is more stable than Amazon EMR.
3. The end to end distributed machine library is more robust in Apache Spark.
4. For very large data sets, Apache Spark is more trustworthy than the other two.
5. For data transformations, Apache Spark provides a very rich set of APIs.
6. The interface provided for SQL in Apache Spark is easy to understand as compared to others.
December 13, 2019
Great open source tool for data processing
We do use Apache Spark for cluster computing for our ETL environment, data and analytics as well as machine learning. It is mainly used by our data engineering team to support the entire Data Lake foundation. As we have huge amounts of information coming from multiple sources, we needed an effective cluster management system to handle capacity and deliver the performance and throughput we needed.
- Cluster management for ETL.
- Data processing engine for our data lake.
- You still need Hive or other HDFS to store information.
- Security is behind compared to MapReduce.
- Simplified our landscape.
- Drove great performance for data processing.
Databricks uses Spark as a foundation, and is also a great platform. It does bring several add-ons, which we did not feel needed by the time we evaluated - and haven't needed since then. One interesting plus in our opinion was the engineering support, which is great depending on the criticality of your platform.