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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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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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(1-21 of 21)Companies can't remove reviews or game the system. Here's why
If you are working on large and big scale data with analytics - don't go further without the use of Apache Spark! One of the projects that I was involved in using Apache Spark was a Recommendation Systems based project. My area or domain of research expertise is also Recommendation Systems. The deployment of a RecSys along with the use of Apache Spark - functionalities like scalability, flexibility of using various data sources along with fault-tolerant systems - are very easy. The built-in machine learning library MLlib is a boon to work. We don't require any other libraries.
- Fault-tolerant systems: in most cases, no node fails. If it fails - the processing still continues.
- Scalable to any extent.
- Has built-in machine learning library called - MLlib
- Very flexible - data from various data sources can be used. Usage with HDFS is very easy
- Its fully not backward compatible.
- It is memory-consuming for heavy and large workloads and datasets
- Support for advanced analytics is not available - MLlib has minimalistic analytics.
- Deployment is a complex task for beginners.
August 30, 2022
Lightning Fast In-Memory Cluster Computing Framework
Score 10 out of 10
Vetted Review
Verified User
Earlier we were using RDBMS like Oracle for retail and eCommerce data. We faced challenges such as cost, performance, and a huge amount of transactions coming in. After a lot of critical issues we migrated to delta lake. Now, we are using Apache Spark Streaming to deal with all real-time transactions. For batch data as well, we are pretty much handling TBs of data using Apache Spark.
- Realtime data processing
- Interactive Analysis of data
- Trigger Event Detection
- Machine Learning
- GraphX Lib
- True Realtime Streaming
Score 9 out of 10
Vetted Review
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.
July 22, 2021
Apache Spark in Telco
Apache Spark is being widely used within the company. In Advanced Analytics department data engineers and data scientists work closely in machine learning projects to generate value. Spark provides unified big data analytics engine which helps us easily process huge amount of data. We are using Spark in projects like churn prediction, network analytics.
- Machine learning on big data
- Stream processing
- Lakehouse with Delta
- Indexing
- Mllib
- Streaming
Apache Spark is used by certain departments to produce summary statistics. The software is used for data sets that are very, very large in size and require immense processing power. The software is also used for simple graphics. When the data are small enough, Apache Spark is not the preferred analytical tool. It's the big data that makes Spark useful.
- Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
- Apache Spark does a fairly good job implementing machine learning models for larger data sets.
- Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
- Apache Spark requires some advanced ability to understand and structure the modeling of big data. The software is not user-friendly.
- The graphics produced by Apache Spark are by no means world-class. They sometimes appear high-schoolish.
- Apache Spark takes an enormous amount of time to crunch through multiple nodes across very large data sets. Apache Spark could improve this by offering the software in a more interactive programming environment.
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
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.
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.
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.
March 16, 2019
Apache Spark Review
We used Apache Spark within our department as a Solution Architecture team. It helped make big data processing more efficient since the same framework can be used for batch and stream processing.
- Customizable, it integrates with Jupyter notebooks which was really helpful for our team.
- Easy to use and implement.
- It allows us to quickly build microservices.
- Release cycles can be faster.
- Sometimes it kicked some of the users out due to inactivity.
December 14, 2018
Apache Spark - defacto for big data processing/analytics
Used as the in memory data engine for big data analytics, streaming data and SQL workloads. Also, in the process of trying it out for certain machine learning algorithms. It basically processes data for analytical needs of the business and is a great tool to co-exist with the hadoop file systems.
- in memory data engine and hence faster processing
- does well to lay on top of hadoop file system for big data analytics
- very good tool for streaming data
- could do a better job for analytics dashboards to provide insights on a data stream and hence not have to rely on data visualization tools along with spark
- also there is room for improvement in the area of data discovery
August 28, 2018
Very useful application for Big Data processing and excellent for large volume production workflows
Apache Spark is being used by the whole organization. It helps us a lot in the transmission of data, as it is 100 times faster than Hadoop MapReduce in memory and 10 times faster in disk, as we work with Java this application. It allows native links for Java programming languages, ​​and as it is compatible with SQL, is completely adapted to the needs of our organization, because of the large amount of information that we use. We highly prefer Apache Spark since it supports in-memory processing to increase performance of big data analysis applications.
- It performs a conventional disk-based process when the data sets are too large to fit into memory, which is very useful because, regardless of the size of the data, it is always possible to store them.
- It has great speed and ability to join multiple types of databases and run different types of analysis applications. This functionality is super useful as it reduces work times
- Increase the information and trainings that come with the application, especially for debugging since the process is difficult to understand.
- It should be more attentive to users and make tutorials, to reduce the learning curve.
- There should be more grouping algorithms.
July 21, 2018
Apache Spark: One stop shop for distributed data processing, machine learning and graph processing
We use Apache Spark across all analytics departments in the company. We primarily use it for distributed data processing and data preparation for machine learning models. We also use it while running distributed CRON jobs for various analytical workloads. I am familiar with a story where we contributed an algorithm to Spark open source which is on Random Walks in Large Graphs - https://databricks.com/session/random-walks-on-large-scale-graphs-with-apache-spark
- Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
- Faster in execution times compare to Hadoop and PIG Latin
- Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
- Interoperability between SQL and Scala / Python style of munging data
- Documentation could be better as I usually end up going to other sites / blogs to understand the concepts better
- More APIs are to be ported to MLlib as only very few algorithms are available at least in clustering segment
June 07, 2018
My Apache Spark Review
My company uses Apache Spark in various ways including machine learning, analytics and batch processing. [We] Grab the data from other sources and put it into a Hadoop environment. [We] Build data lakes. SparkSQL is also used for analysis of data and to develop reports. We have deployed the clusters in Cloudera. Because of Apache Spark, it has become very easy to apply data science in a big data field.
- Easy ELT Process
- Easy clustering on cloud
- Amazing speed
- Batch & real time processing
- Debugging is difficult as it is new for most people
- There are fewer learning resources
March 27, 2018
Apache Spark, the be all End All.
Spark was/is being used in myriad of ways. With Kafka, using Spark Streams to grab data from kafka queue into our hdfs environment. SparkSQL used for analysis of data for those not familiar with spark. Using Spark for data analysis as well and for main workflow process. Using spark over mapreduce. Using Spark for some machine learning algo's with the data.
- Machine Learning.
- Data Analysis
- WorkFlow process (faster than MapReduce).
- SQL connector to multiple data sources
- Memory management. Very weak on that.
- PySpark not as robust as scala with spark.
- spark master HA is needed. Not as HA as it should be.
- Locality should not be a necessity, but does help improvement. But would prefer no locality
January 23, 2018
Use Apache Spark to Speed Up Cluster Computing
In our company, we used Spark for a healthcare analytical project, where we need to do large-scale data processing in a Hadoop environment. The project is about building an enterprise data lake where we bring data from multiple products and consolidate. Further, in the downstream, we will develop some business reports.
- We used to make our batch processing faster. Spark is faster in batch processing than MapReduce with it in memory computing
- Spark will run along with other tools in the Hadoop ecosystem including Hive and Pig
- Spark supports both batch and real-time processing
- Apache Spark has Machine Learning Algorithms support
- Consumes more memory
- Difficult to address issues around memory utilization
- Expensive - In-memory processing is expensive when we look for a cost-efficient processing of big data
December 13, 2017
Apache Spark Should Spark Your Interest
At my current company, we are using Spark in a variety of ways ranging from batch processing to data analysis to machine learning techniques. It has become our main driver for any distributed processing applications. It has gained quick adoption across the organization for its ease of use, integration into the Hadoop stack, and for its support in a variety of languages.
- Ease of use, the Spark API allows for minimal boilerplate and can be written in a variety of languages including Python, Scala, and Java.
- Performance, for most applications we have found that jobs are more performant running via Spark than other distributed processing technologies like Map-Reduce, Hive, and Pig.
- Flexibility, the frameworks comes with support for streaming, batch processing, sql queries, machine learning, etc. It can be used in a variety of applications without needing to integrate a lot of other distributed processing technologies.
- Resource heavy, jobs, in general, can be very memory intensive and you will want the nodes in your cluster to reflect that.
- Debugging, it has gotten better with every release but sometimes it can be difficult to debug an error due to ambiguous or misleading exceptions and stack traces.
October 26, 2017
Apache Spark - Simple Syntax, Huge Data Handling, Best Optimization, Parallel processing
We previously used the database and Pentaho ETL tool to perform data transformation as per project requirements but as the time passed our data is building day by day and we suffered a lot of optimization problems working this way. Then we thought of implementing Hadoop cluster with 8 nodes in our company. We deployed an 8 node cluster with Cloudera distribution. Then we started using Apache Spark to create applications for Student Course Enrollment data and run them parallelly on multiprocessors.
It is used by a department but the data consists of information about students and professors of the whole organization.
It addresses the problem of assigning classrooms for a specific time in a week based on student course enrollment and professors teaching the course schedules.
This is just one aspect of the application. There are various other data transformation requirement scenarios for different departments across the organization
It is used by a department but the data consists of information about students and professors of the whole organization.
It addresses the problem of assigning classrooms for a specific time in a week based on student course enrollment and professors teaching the course schedules.
This is just one aspect of the application. There are various other data transformation requirement scenarios for different departments across the organization
- Spark uses Scala which is a functional programming language and easy to use language. Syntax is simpler and human readable.
- It can be used to run transformations on huge data on different cluster parallelly. It automatically optimizes the process to get output efficiently in less time.
- It also provides machine learning API for data science applications and also Spark SQL to query fast for data analysis.
- I also use Zeppelin online tool which is used to fast query and very helpful for BI guys to visualize query outputs.
- Data visualization.
- Waiting for Web Development for small apps to be started with Spark as backbone middleware and HDFS as data retrieval file system.
- Transformations and actions available are limited so must modify API to work for more features.
August 02, 2017
Apache Spark if great for high volume production workflows
We use it primarily in our department as part of a machine learning and data processing platform to build enterprise scale predictive applications.
- Great APIs and tools.
- Scale.
- Speed for iterative algorithms.
- No true streaming.
- Lack of strongly typed yet convenient APIs.
June 26, 2017
Sparkling Spark
It's being replaced as the traditional ETL tool and we are using Apache Spark for data science solutions.
- It makes the ETL process very simple when compared to SQL SERVER and MYSQL ETL tools.
- It's very fast and has many machine learning algorithms which can be used for data science problems.
- It is easily implemented on a cloud cluster.
- The initialization and spark context procedures.
- Running applications on a cluster is not well documented anywhere, some applications are hard to debug.
- Debugging and Testing are sometimes time-consuming.
September 12, 2016
A useful replacement for MapReduce for Big Data processing
We are learning core Apache Spark + SparkSQL and MLLib, while creating proof-of-concepts as well as providing solutions for clients. It addresses the needs of quickly processing large amounts of data, typically located in Hadoop.
- Scale from local machine to full cluster. You can run a standalone, single cluster simply by starting up a Spark Shell or submitting an application to test an algorithm, then it quickly can be transferred and configured to run in a distributed environment.
- Provides multiple APIs. Most people I know use Python and/or Java as their main programming language. Data scientists who are familiar with NumPy and SciPy can quickly become comfortable with Spark, while Java developers would best served using Java 8 and the new features that it provides. Scala, on the other hand, is a mix between the Java and Python styles of writing Spark code, in my opinion.
- Plentiful learning resources. The Learning Spark book is a good introduction to the mechanics of Spark although written for Spark 1.3, and the current version is 2.0. The GitHub repository for the book contains all the code examples that are discussed, plus the Spark website is also filled with useful information that is simple to navigate.
- For data that isn't truly that large, Spark may be overkill when the problem could likely be solved on a computer with reasonable hardware resources. There doesn't seem to be a lot of examples for how a Spark task would otherwise be implemented in a different library; for instance scikit-learn and NumPy rather than Spark MLlib.