When considering the various engines in the Hadoop ecosystem, it's important to understand that each engine works best for certain use cases, and that organizations may need to use a combination of tools in order to meet each desired use case. That said, here is a review of some of the top use cases for Apache Spark.

I. Streaming Data

The key use case for Apache Spark is its ability to process streaming data. Streaming and analyzing data in real time has become critical for companies because of the amount of data that is processed on a daily basis.Spark Streaming has the ability to handle this additional workload. Some experts even believe that Spark can be the platform of choice for streaming applications regardless of the type. The reason for this claim is that Spark Streaming unifies different data processing capabilities so that developers can use a single framework for all their processing needs.

The general ways in which organizations are using Spark Streaming today include:

1. Streaming ETL - Traditional ETL (Extract, Transform, Load) tools used for batch processing in data warehouse environments have to read the data, convert it to a database-compatible format, and then write it to the target database. With Streaming ETL, data is continuously cleaned and aggregated before it is pushed to the data store.

2. Data Enrichment - This Spark Streaming feature enriches real-time data by combining it with static data, enabling organizations to perform more complete real-time data analysis. Online advertisers use Data Enrichment to combine historical customer data with real-time customer behavior data, and to deliver more personalized and targeted ads in real-time based on customer behavior.

3. Trigger Event Detection - Spark Streaming enables organizations to detect and respond quickly to rare or unusual behaviors ("trigger events") that may be potentially serious problems within a system. Financial institutions use triggers to detect fraudulent transactions and stop them from being fraudulent. Hospitals also use triggers to detect potentially dangerous health changes while monitoring patients' vital signs - sending automated alerts to the right caregivers, who can then take appropriate action immediately.

4. Complex session analytics - Using Spark Streaming, events related to real-time sessions, such as user activity after logging into a website or application, can be combined and analyzed quickly. Session information can also be used to continually update machine learning models. Companies such as Netflix use this feature to instantly understand how users are engaging on their sites and provide more real-time movie recommendations.

Two: Machine learning

Another one of the many Apache Spark use cases is its machine learning capabilities.

Spark comes with an integrated framework for performing advanced analytics that helps users perform repetitive queries on datasets, which essentially deals with machine learning algorithms. Components found in this framework include Spark's Extensible Machine Learning Library (MLlib).MLlib can work in areas such as clustering, classification and dimensionality reduction. All of this allows Spark to be used for some very common big data functions such as predictive intelligence, customer segmentation for marketing purposes, and sentiment analysis. Companies using recommendation engines will find that Spark gets the job done quickly.

Cybersecurity is a great business case for Spark's machine learning capabilities. By using various components of the Spark stack, security providers can perform real-time inspection of packets for traces of malicious activity. On the front end, Spark Streaming allows security analysts to examine known threats before passing packets to the storage platform. Upon reaching the storage, the packets are passed through other stack components (e.g., MLlib) for further analysis. As a result, security providers can learn about new threats as they evolve-always staying ahead of hackers while protecting their customers in real time.

Three: Interactive Analytics

One of Spark's most notable features is its interactive analytics capabilities. mapReduce is built to handle batch processing, and SQL-on-Hadoop engines such as Hive or Pig are typically too slow to perform interactive analytics. However, Apache Spark is fast enough to perform exploratory queries without sampling.Spark also interfaces with a variety of development languages including SQL, R, and Python. By using Spark in conjunction with visualization tools, complex datasets can be processed and visualized interactively.

The next version of Apache Spark (Spark 2.0), which will debut in April or May of this year, will have a new feature -- structured streams -- that enables users to perform interactive queries on real-time data. By combining real-time streaming with other types of data analytics, structured streaming is expected to facilitate Web analytics by allowing users to run interactive queries against a Web visitor's current session. It can also be used to apply machine learning algorithms to real-time data. In this case, algorithms would be trained on old data, which would then be redirected to merge new data and learn from it as it comes into memory.

Four, Fog Computing

While big data analytics may get a lot of attention, the concept that has really sparked the imagination of the technology community is the Internet of Things (IoT). The IoT embeds objects and devices through miniature sensors that communicate with each other and with users, creating a fully interconnected world. This world collects huge amounts of data, processes it, and provides revolutionary new features and applications for people to use in their daily lives. However, as the IoT expands, the need for massively parallel processing of large, diverse machine and sensor data increases. However, it is difficult to manage all of this processing using current analytics capabilities in the cloud.

That's where fog computing and Apache Spark come in.

Fog computing decentralizes data processing and storage rather than performing these functions at the edge of the network. However, fog computing introduces a new level of complexity to processing decentralized data, as it increasingly requires low latency, massively parallel processing for machine learning, and extremely complex graph analytics algorithms. Fortunately, with key stack components such as Spark Streaming, an interactive real-time query tool (Shark), a machine learning library (MLib), and a graphical analytics engine (GraphX), Spark not only qualifies as a fog computing solution. In fact, as the IoT industry gradually and inevitably converges, many industry experts predict that Spark has the potential to become the de facto fog infrastructure compared to other open source platforms.

Real-world Spark

As mentioned earlier, online advertisers and companies such as Netflix are utilizing Spark to gain insight and a competitive advantage. Other notable companies that are also benefiting from Spark are:

Uber - The multinational online cab dispatch company collects terabytes of event data from its mobile users every day. By building a continuous ETL pipeline using Kafka, Spark Streaming, and HDFS, Uber can convert raw unstructured event data into structured data as it is collected, and then use it for further and more complex analysis.

Pinterest - With a similar ETL pipeline, Pinterest can utilize Spark Streaming to instantly understand how users around the world are interacting with Pins. As a result, when people browse the site and view relevant pins, Pinterest can make more relevant suggestions to help them choose recipes, identify products to buy or plan trips to various destinations.

Conviva - This streaming video company averages about 4 million video feeds per month, second only to YouTube, and Conviva uses Spark to reduce customer churn by optimizing video streams and managing real-time video traffic to maintain a consistently smooth, high-quality viewing experience.

When not to use Spark

Despite its versatility, this doesn't necessarily mean that Apache Spark's in-memory capabilities are best suited for all use cases. More specifically, Spark, an application instance of Apache Spark for Big Data analytics, is not designed as a multi-user environment. the Spark users need to know whether the memory they are entitled to access is sufficient for the dataset. Adding more users complicates this operation because users must coordinate memory usage in order to run projects concurrently. Unable to handle this type of concurrency, users will need to consider an alternate engine, such as Apache Hive, for large batch projects.

Apache Spark will continue to evolve its ecosystem as time goes on, becoming more versatile than ever before. In a world where big data has become the norm, organizations will need to find the best ways to leverage it. As you can see from these Apache Spark use cases, there will be many opportunities to see what Spark can really do in the coming years.

As more and more organizations recognize the benefits of transitioning from batch processing to real-time data analytics, Apache Spark is positioned to gain widespread and rapid adoption across a wide range of industries.