Apache Spark Reviews

Most of my use cases involve data processing. For example, someone tried to run sentiment analysis on Databricks using Apache Spark. They had to handle data from many countries and languages, which presented some challenges. Besides that, I primarily use Apache Spark for data processing tasks. I work with mobile phone datasets, around one terabyte in size. This involves extracting and analyzing data before building any models.

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming.

Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

I have some hands-on experience with Spark. I have one year of experience that should be considered as one year working with Spark, which is six months to one year. We use it for faster processing, especially compute. 

Spark is used for transformations from large volumes of data, and it is usefully distributed. We receive data from various sources and need to transform it. The data is enormous, in terabytes, and often from specific databases. We perform transformations, aggregations, and deduplication. 

We meet business requirements by computing data, minimizing it, aggregating it, or performing other operations. We typically write to Hive downstream.

Spark is faster and distributed. Previously, everything relied on MapReduce, which was slower. With Spark, multiple computations and transformations hold in memory for faster processing. 

Real-time communication is possible, connecting with platforms like Kafka for real-time data import and compute. We implemented Spark and NiFi for integration. Spark replaced other costly products, reducing costs by thirty-eight percent.

The only issue I faced with the tool was that I used to choose the compute device to support parallel processing, and it has to be more like scaling up horizontally. The tool should be more scalable, not in terms of increasing the CPU or something, but more in the area of units. If two units are not enough, the third or fourth unit should be able to come into the picture.

From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable. Sometimes, I get an error saying that it is an RDD-related error, and it becomes difficult to understand where it went wrong. When I deal with datasets using a library called Pandas in Python, I can actually apply functions on each column and get a transformation from the column. When I try to do the same thing with Apache Spark, it is okay and works, but it is not straightforward; I need to deal with it a little differently, and even after trying to do that differently, the problem I face there is, sometimes it will throw an error saying that it is looping back to the same, but I was not getting that kind of errors in Pandas.

In future updates, the tool should be made more user-friendly. I want to take fifty parallel processes rather than one, and I want to pick some particular columns to be split by partition, so if the tool is user-friendly and offers clarity and flexibility, then that will be good.

I have been using Apache Spark for four years.

The primary use case for Apache Spark is to process data in memory, using big data, and distributing the engine to process said data. It is used for various tasks such as running the association rules algorithm in ML Spark ML, running XGBoost in parallel using the Spark engine, and preparing data for online machine learning using Spark Streaming mode.

The most significant cost savings come from the operational side because Spark is very typical in operations. There are many experts available in the market to operate Spark, making it easier to find the right personnel. It is quite mature, which reduces operation costs.

We use it for real-time and near-real-time data processing. We use it for ETL purposes as well as for implementing the full transformation pipelines.

There is no other platform that can challenge its features. Apart from the restrictions that come with its in-memory implementation.

We use the solution to extract data from our sensors.  We have lots of data streaming into our system, which used to get overwhelmed. We use Apache Spark to handle real-time streaming and do machine learning to predict supply and demand in the market and adjust operations.

I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems.

The tool's real-time processing has had a big impact. We used to get data from sensors after a month. We get it in less than 10 minutes, which helps us take quick action.

We use Apache Spark to map our data pipelines using MapReduce technology. We're also working on integrating tools like Hive with Apache Spark to distribute our data processing. We can also integrate other tools like Apache Kafka and Hadoop.

We faced some challenges when integrating the solution into our existing system, but good documentation helped solve them.

Our main use cases for Spark are Apache Spark SQL and sometimes Spark Streaming to process streaming data.

Like most solutions, we got data from SAP or Azure Data Warehouse. Suppose they were using Azure Cloud technology. So, the data comes from there, relational or sometimes semi-structured data like JSON files and all. 

So, we process the data with Spark, writing this code with PySpark, actually Python, which Spark allows, to create the data forms and all and load it into the Tableau format, basically. 

So, we try to load it into some database, like SQL Server or any other database. From there, the business data scientists or analysts pick up the data. So, any sort of different sources, basically, like e-commerce sites. 

So, previously, we used mostly structured data, which was stored in SAP, mainframe Oracle, or any other system provided in structured formats like CSV. 

Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark.

Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more.

Before Spark, there was MapReduce, but it was much slower. Even running the same query a second time would be time-consuming due to the I/O operations with disk storage. Spark was introduced to address these issues, offering processing speeds a hundred times faster than MapReduce, an initiative that saw contributions from Adobe Systems among others.

So, in response to the evolving needs of the industry, Spark has proven to be the solution, efficiently handling the processing requirements we face today.

Spark supports real-time data processing through Spark Streaming. It allows for batch processing of data. If you have immediate data, like chat information, that needs to be processed in real-time, Spark Streaming is used. 

For data that can be evaluated later, batch processing with Apache Spark is suitable. Mostly, batch processing is utilized in our organization, but for streaming data processing, tools like Kafka are often integrated.

In-memory processing in Spark greatly enhances performance, making it a hundred times faster than the previous MapReduce methods. This improvement is achieved through optimization techniques like caching, broadcasting, and partitioning, which help in optimizing queries for faster processing.