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Apache Spark is an incredibly versatile tool that has been widely adopted across various departments for processing very large datasets …
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The National Institute of Engineering, Mysuru (Education Management, 501-1000 employees)
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Spark Project | Spark Tutorial | Online Spark Training | Intellipaat
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Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginners | Simplilearn
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Apache Spark Full Course | Apache Spark Tutorial For Beginners | Learn Spark In 7 Hours |Simplilearn
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Apache Spark Architecture | Spark Cluster Architecture Explained | Spark Training | Edureka
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Introduction to Databricks [New demo linked in description]
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Apache Spark Tutorial | Spark Tutorial for Beginners | Spark Big Data | Intellipaat
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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.
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Score 9 out of 10
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Verified User
We need to calculate risk-weighted assets (RWA) daily and monthly for different positions the bank holds on a T+1 basis. The volume of calculations is large: more than millions of records per day with very complicated formulas and algorithms. In our applications/projects, we used Scala and Apache Spark clusters to load all data we needed for calculation and implemented complicated formulas and algorithms via its DataFrame or DataSet from the Apache Spark platform.
Without adopting the Apache Spark cluster, it would be pretty hard for us to implement such a big system to handle a large volume of data calculations daily. After this system was successfully deployed into PROD, we've been able to provide capital risk control reports to regulation/compliance controllers in different regions in this global financial world.
Without adopting the Apache Spark cluster, it would be pretty hard for us to implement such a big system to handle a large volume of data calculations daily. After this system was successfully deployed into PROD, we've been able to provide capital risk control reports to regulation/compliance controllers in different regions in this global financial world.
- DataFrame as a distributed collection of data: easy for developers to implement algorithms and formulas.
- Calculation in-memory.
- Cluster to distribute large data of calculation.
- It would be great if Apache Spark could provide a native database to manage all file info of saved parquet.
- The speed of processing a large volume of data.
- Dataframe with SQL-like operations reduces the learning curve for new developers if they do have very good knowledge of databases and SQL.
- Cluster to scale up/down easily.
- With the daily risk reports being calculated via Apache Spark, the bank is able to comply with the FHC rule in the US and other regions and control capitals much better with counterparties.
Other teams used to work on Apache Hadoop but our team started with Apache Spark directly.
100
Regulation/compliance for capital risk control.
10
This is supported by our EI team.
- Large volume of data computing.
- Speed of computing.
- Extend it to other computing in different departments.