How can you use the latest data transformation tools?

GCP provides pub/sub data pipeline option to cater the below 4 biggest challenges of real time streaming data - 1. Variety - it caters multiple data sources at one 2. Volume - it can handle large volumes of data 3. Velocity - the rate at which data enters from sources 4. Veracity - data noises, biases and abnormalities Data engineers can create pipelines using GCS buckets and deliver data into Bigquery. Streaming data could be easily analysed using SQL queries. Lastly - tools like looker can be integrated with bigquery to gain business insights

Data transformation tools are essential components in the realm of data management and analytics. These tools facilitate the process of converting raw, disparate data into a structured and usable format for analysis. By employing techniques such as cleaning, aggregating, and enriching data, they ensure that information is accurate and relevant. Popular examples include ETL (Extract, Transform, Load) tools, which streamline the movement and transformation of data across various systems. Ultimately, data transformation tools play a crucial role in unlocking actionable insights and enhancing decision-making processes within organizations.

Data transformation is something you, as a data analyst, are gonna do even more than the data analysis itself. Now, imagine spending 80% of your time transforming raw data into a clean and ready-to-be-analyzed form before performing your actual task. Well, this is pretty exhausting, but that's where handy transforming tools come into play. Informatica, Trifacta, and Datameer are some I would mention. These tools can accelerate and automate the data transformation process, making it happen in minutes rather than hours.

You should use data transformation tools because you need to transform data from its original form to a new form. I have encountered this countless times in my work as a data person. In fact, you might say that most of the job you're doing as a data person is transforming data from it's raw form stored in a database to a usable form for driving business impact.

Data transformation follows a structured process regardless of the tool used. Start by setting clear objectives for the data you aim to modify. Choose appropriate data sources and destinations, like databases. Design transformation pipelines using the tool's GUI, incorporating necessary functions. Monitor pipeline execution via the GUI and, upon completion, validate the transformed data's quality and accuracy using the same interface.

As you gather requirements, document them in a relevant schema transformation language like json schema or xml schema. These are "data dictionaries" that both machines and (trained) humans can read. With the right tooling they make the actual transformation unambiguous, and impact analysis for subsequent changes straightforward. If you go this route, use a tool. Most schema languages are a pain visually, and small errors make a big difference. Just because you're "wo/man enough" to do it all from the console, doesn't make that productive ;-)