If we conduct a survey around big organizations of the world and ask them what is the most important asset for an organization, well guess what the answer would be? Around 97% of organizations would vote in favor of Data. What would be the reason for that? Any guesses? No, right? Well, it is just because organizations run on data.
With data being this important, there must be some very safe and sound means to handle it. As the handling of the data can make or break the deal for you. There have been some methods of handling data manually and it worked as well because the amount of data never used to be this huge before. Keeping in mind the fact that data is not so small in amount, handling it manually is not an option anymore. These manual methods have been replaced by data engineering tools now, and it looks like that was all we ever needed. Now that we have these data analytics tools, the question that arises is which one to use? Well, this depends on the amount of our data, type of data. And also the operations we will perform using these tools.
Top 5 Tools for Data Engineers
When we talk about data analytics tools, it is a tough job to list down top data analytics tools. What we can do is we can shortlist them based on which are more popular, more user-friendly, and more performance-oriented. It would be a hard nut to crack for a data engineer to choose one of them. From the never-ending list of tools, we are going to cover the top 5 here. Which are contingent upon the type of operations we are going to perform. According to all the characteristics mentioned above, the top 5 analytics tools are as follows.
- SAS
When we talk about statistical operations and data analysis, SAS is the first name that pops up. It was developed and introduced by the SAS institute back in 1966. And then it was further developed in the 1990s. It is very easily accessible, manageable, and we can analyze data from any source by using this tool. There you can find numerous statistical libraries, and if you are a data engineer, you can effortlessly use these to model, analyze, and organize the data. It supports multiple programming languages, and you can comfortably perform operations using any of them. SAS is also used to predict behaviors and optimize communication.
- Apache Spark
If we discuss about the most used data engineering tool, Apache Spark is a definite name to come up. It is a very robust analytics tool that does not only work with the batch processing but also the stream processing. There are many APIs available in Apache Spark that makes you able to access data multiple times for machine learning. It is an improved version of Apache Hadoop and works 100 times faster than Hadoop's MapReduce. If given with appropriate data, we can utilize it to obtain significant assessments as well. MLlib is the library that we use in Apache Spark.
- Excel
Excel is a tool that we all have used at least once in our lives. So, there is nothing to talk about its popularity. It is a tool developed by Microsoft, and we mostly used it for making spreadsheets. Little did we know it can be applied to visualize and process data. It is a powerful analytics tool when it comes to complex data calculations. There a lot of built-in formulae, slicers, and tables are available in Excel, and you can also make your customized formulae in it. In data science, it can also work as a data cleaner due to the interactable GUI environment.
- Matlab
Matlab is a high-performance technical computing environment that integrates programming, visualization, and computation. It is mostly suited for the implementation of algorithms and modeling of statistical data. If we talk about data science, it is applied to simulate neural networks and fuzzy logic. We can also do the image processing by using graphic libraries of Matlab. These libraries and other features make it a very suitable tool when it comes to data engineering. A data scientist can use for multiple things, from data analysis to solving complex deep learning algorithms.
- Python
Python is an open-source, object-oriented language, and it is very smooth to write, read, and manage. Python is a very efficient tool when it comes to data science. It has a lot of libraries that make it easy to model and analyze data. It is very advantageous in solving complex problems as we can integrate it with any existing infrastructure.
How to Ace Them?
When it comes to ace a tool or becoming a pro in something, and old proverb comes to the rescue. That being, Practice makes a man perfect. It is valid in a way, but there are other things we can apply if we want to ace a tool. These things can be used to ace not only a tool but anything in life. Let's discuss it further.
- Watch Tutorials Online
If you want to be good at something, learn how to do it. And, the best way to do it is by watching tutorials online. If you do this, you can learn the most efficient way of doing it.
- Keep Working with the Tool
There is another way of doing this by keeping yourself engage with the tool you want to learn. You can apply the knowledge you gathered from the tutorials you watched. And you can also find your way of doing it by just practicing and trying.
If you are a data engineer or have any plans on becoming one, you should develop expertise in one or more of the data tools mentioned above. Anyone can surely achieve it by working on the tips, introduced just below those tools. Or, you can always enroll in a data science academy to become a data engineering expert. By getting enrolled in data engineer training, you learn way more than just these tools.