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In the field of data science, one can easily agree with the fact that most amount of the time building a data science project usually goes to the pre-processing of data. More than 50% of the time is spent in the data processing and the feature engineering part while comparatively less amount of time is considered on building the model.
The pre-processing or the feature engineering part is mostly manually done using the tools available in Python such as Jupyter notebooks and packages such as pandas, and NumPy matplotlib. These packages are efficient and work with featuring engineering, but with a high volume of data, the following packages are usually challenging to be used.
To overcome the use of these bottlenecks AWS provided a codeless architecture in a graphical user interface format known as AWS glue DataBrew.
What is AWS DataBrew?
AWS data brew provides a graphical interface to transform, inspect, and wrangle data without coding. The interface of DataBrew is quite easy and convenient to use as it is also a scalable and fully managed service. Along with the capabilities of cleaning data, it also helps you with normalizing data and scaling data for analytics and machine learning. Any tasks that are performed on the DataBrew service can be automated. For example, you can automate filtering which makes it a quicker tool for data preparation
Capabilities of DataBrew
Mentioned below are some steps and functionalities DataBrew employs for data preparation.
Data profiling is an important step in any data analysis project. Data profiling helps us understand the features of the data set. Python provides a wide range of libraries to help with the profiling part, some of which are pandas profiling, and sweetviz. Though these libraries provide a detailed report of the data, the major drawback is the time of execution on larger data sets. A data profiling job in DataBrew can be done on any data stored in data lakes or S3, the output report for further reference is stored in S3. The profiled data contains the information statistics of the data from correlation to different visualizations and graphs as desired by the user.
- Data lineage
Data lineage provides a map-like architecture flow that demonstrates the flow of the execution of data. This helps to keep in check the data and the transformation steps that have been applied to the data from the source to the output. The map lineage provides a simple yet effective mechanism to understand the flow of data in a graphical form.
- Clean and Normalize
Normalization is used in machine learning to scale and convert the numeric features in a data set nomination can be done by bringing the numeric data to a common scale during data preparation. Normalization it’s performed only when required. Not all machine learning models need normalization. It is mostly used when the features have different ranges. Data cleaning is the most essential part of building a model. It can range from removing duplicates to performing interpolation on various missing data. Good clean data helps in building a better model.
The automation part of AWS data brew is one of the best functions which can be used on data. This helps an automating the data branding process and normalization by applying transformations directly to incoming data. This is time and reusability of code as the incoming data would be directly filtered, this makes the machine learning process much faster.
The outcome for any machine learning technique is to build a better model, this can only leave it here if the dataset is properly cleaned and transformed. Inaccurate features, duplication, missing values in source data, or ingested data make it impossible to be used in raw form. This is where AWS DataBrew helps by providing an advanced mechanism for feature engineering tasks. This tool helps data scientists derive more meaningful insights in a short period enhancing business and growth.
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Drop a query if you have any questions regarding DataBrew and I will get back to you quickly.
WRITTEN BY Bineet Singh Kushwah