You’ve probably heard by now that “big data” will be the next big thing. It’s the best thing that’s happened to bread in a long time! The world we live in is being transformed by it!
These statements have some truth to them, but what is often left out is the amount of effort required to process big data. If you only read the headlines, you might think that big data is an endless well of wisdom that can do nothing but improve our lives. The truth is that big data is a jumbled mess; in fact, that’s what the term means. Big data is shorthand for datasets that are too large, complex, and disorganized to be processed conventionally.
Luckily, there is a never-ending army of data scientists and analysts to help sort through the chaos of raw data and the revolutions we read about. What exactly is it that they do? In a low voice, establish some sort of order among the mayhem. It is their job to take massive amounts of data and organize them in ways that we can use. Time, knowledge, and perseverance are all necessities. Thankfully, there is a plethora of big data tools to aid in the process. Only a few of our favorite big data resources are covered here.
- Apache Spark
- Apache Hadoop
- Apache Flink
- Google Cloud Platform
Is everyone aboard ready to go for a spin? Don your seatbelts and hang on as we explore the seven most important big data tools of 2022.
Free and open-source.
The range of possible deployment strategies is quite large.
The Apache Software Foundation is a US-based charity that funds a wide range of free and open-source software initiatives. To make and keep these tools up-to-date and innovative, an open community of programmers is responsible for their creation and upkeep. Apache Spark is among the most well-known tools developed by the Apache Software Foundation.
Spark was introduced in 2012 as a unified analytics engine tailored for handling large datasets through distributed computing. It can perform either real-time or batch processing. It has high-level APIs for R, Java, Python, and Scala, as well as data streaming modules, SQL, and machine learning algorithms built right in (meaning you can use your preferred language when programming). Spark’s open-source nature and extensive pre-built features make it suitable for use in any area where data science is applied.
To begin with, Apache developed Spark to remedy problems with Hadoop MapReduce. Spark is more effective and flexible than its predecessors, and it can handle batch and real-time processing with essentially the same code. Therefore, big data tools that were developed before this functionality was added are becoming increasingly irrelevant. According to Apache, Spark is 100 times faster than MapReduce and can process 100 terabytes of big data in a third of the time with one-third of the required hardware. In case you haven’t heard of Spark, you’re missing out. Have no apprehension: you will!
Cost nothing and is open source.
There are numerous deployment strategies to choose from.
While we did just say that Apache Spark is more effective than other big data tools (especially Apache Hadoop), this does not mean that Hadoop is completely useless. Hadoop is an open-source framework that stores and processes large amounts of data in a similar fashion to Spark’s distributed file system and MapReduce engine. Despite the fact that the framework is slower than Spark and has been around since 2006, many companies that have already adopted Hadoop are unlikely to suddenly switch to a newer, more modern alternative.
Hadoop also has some advantages. You can rest assured that it works because, to begin with, it has been tried and tested. Despite its flaws (such as its inefficiency in handling smaller datasets and real-time analytics), the software is solid and dependable. Hadoop is not limited to or dependent upon supercomputers, and can instead be deployed on a wide variety of standard hardware. Lastly, it is cost-effective to maintain because of its ability to spread out both storage and workload. And if that weren’t enough, a lot of business cloud services still back Hadoop. Analytics Engine by IBM, for instance. This is, therefore, a possible resource to come across as you explore the world of data analytics.
Free and open-source.
The range of possible deployment strategies is quite large.
We hate to be a broken record, but there is still one more Apache big data tool that deserves a nod: (although there are literally dozens to choose from). Also released in 2011, Apache Flink is another unified processing framework that is free and open-source for anyone to use. Similar to Spark, it can perform both batch and stream processing. The main distinction is that Flink uses streaming to execute both batch and streaming jobs in a pipelined fashion, whereas Spark relies on batch processing for both. Without getting too deep into the weeds of the technical details, this simply means that Flink can process data at a much faster rate and with lower latency (or delay) than Spark, which measures latency in seconds as opposed to microseconds.
In no way does this imply that Flink is superior to Spark. When it comes to the speed at which they can process large amounts of data, both are heads and shoulders above the rest. Spark also enjoys much more widespread support than Flink does, as it is supported by all major Hadoop frameworks while Flink is not. No matter how many times the topic of Flink’s eventual dominance over Spark is brought up, the reality is that both can coexist successfully. There are many great big data tools in the Apache ecosystem, but we need to broaden our focus for a while.
Prices begin at $0.001, but a free version and a trial period are both available.
Cloud, Mac, Windows desktop/mobile, and other deployment options (Android and Apple).
Google Cloud Platform unifies a number of Google’s own cloud computing services, including Google Search, Gmail, YouTube, and Google Docs, in order to compete with Amazon Web Services (to name a few). In spite of the platform’s lack of focus on big data, it does include a number of big data tools, such as Dataflow (a managed streaming analytics service) and Data Fusion (for building distributed data lakes via the integration of on-premise platforms).
BigQuery stands out among the rest because it is a fully managed analytics data warehouse that can store data at the petabyte scale. It is a service platform with built-in machine learning tools that facilitates near real-time processing of massive amounts of big data. Users of BigQuery have the freedom to make and destroy any number of objects, from tables and views to user-defined procedures. Multiple data formats, such as CSV, Parquet, Avro, and JSON, can be imported.
BigQuery is especially user-friendly because it can be easily integrated with existing SQL infrastructure. One major drawback is that it lags behind competing platforms in adopting new features and technologies. Although this may seem like a lot, when you consider the low cost, scalability, and predefined configurations, it’s a minor inconvenience for most applications. It’s also used by a lot of different businesses, making it difficult to leave.Start learning for free!
Pricing is on a per-feature basis, and there is a free version available for testing purposes.
Cloud, desktop (Mac, Windows, Linux), and on-premise deployment options.
If you need help organizing massive amounts of data, look no further than Google Cloud’s BigQuery; if you need a flexible, scalable non-relational database, look no further than MongoDB (also known as a NoSQL database.) This merely denotes that it is more suited to dealing with documents containing unstructured big data than traditional tabular data stored in rows and columns (as used in relational databases). MongoDB is widely used by both startups and large corporations as a big data tool.
Why use MongoDB? Quickly deploy and use, with minimal effort. In addition, it is schema-free (i.e., it does not need to conform to a particular data type), which is advantageous because it reduces setup time and facilitates the management of unstructured data. While it has some issues (a sluggish search, for example), the developers behind it are what keep users coming back. They have great customer service and are always putting new features and updates into the product. It appears to have paid off, as MongoDB is now the most downloaded NoSQL database, enabling a wide range of users to query, manipulate, and analyze their unstructured data.
Price: Quotes are provided upon inquiry. There’s also the option to try it for free.
Cloud, on-premises (Windows and Linux), desktop, and mobile are all viable deployment options.
We should warn you that using some of the items on our list, like the Apache big data tools, will require some familiarity with programming. But if you want a big data tool that doesn’t call for any technical know-how, Sisense’s Big Data platform might be what you’re looking for. It is “the only Big Data analytics tool and data visualization tool that enables business users, analysts, and data engineers to prepare and analyze massive amounts of data in a fraction of the time,” as stated on the company’s website. data on a terabyte scale from a variety of sources, with no additional software, hardware, or trained personnel.” Sounds too good to be true.
Sisense bridges the gap between big data management tools and data visualization and analytics tools. The tool has a quick analytic database, in-built ETL tools, Python, and R, and a robust data analysis and data visualization suite, and it can be implemented in a variety of industries, including healthcare, manufacturing, and retail. All of its flaws are in places where you’d reasonably anticipate compromises to be made in exchange for its simple functionality. For instance, the amount of control you have over the appearance of the drag-and-drop interface of the dashboard is quite low. Sisense is a good business intelligence tool once you get it up and running and get used to its quirks. It has a few stability issues and set up can be complex, but other than that, it works well.
Costs: Costs available upon request. There is a free version and a trial version that costs nothing.
cloud, desktop (Mac, Windows), and on-premise deployment options (Windows, Linux).
RapidMiner, like Sisense, aims to equip data professionals of varying skill levels with the means to rapidly prototype data models and execute machine learning algorithms without the need for coding expertise. Through a visually cohesive process-oriented layout, it unifies data access, mining, preparation, and predictive modeling. RapidMiner is a Java-based platform, making it compatible with a wide variety of other java applications; however, the no-code approach may prove to be a bit of a learning curve for more experienced programmers. However, it does support coding-based customization via its Python and Java modules.
RapidMiner’s interface is more user-friendly for students, but for those who need it, there are support packages (although these do cost quite a lot extra). You can add more features and functionality to the program as your users become more comfortable with using it. Maybe its biggest flaw is that it can’t deal with huge amounts of information very well… not the best option for a database management system. The low barrier to entry means we couldn’t leave it off the list. If you need a quick solution for your big data problems, try RapidMinder.
The Final Word
It is our sincere hope that this compilation of tools, ranging from those that require extensive knowledge of code to those that require none, has piqued your interest. If you dig deeper, you’ll discover that the market is flooded with a wide variety of big data tools, each suited to a specific set of requirements and applications.