Reference no: EM133547841

Parallel data processing and data wrangling

Data management uning python is essential for converting raw data into an analyzable format. In the case of large or complex data sets, parallel data management can speed up the process significantly and result of the answer that are analytzed. Here's a brief method with examples from the Chicago taxi ride dataset:

Step 1:

Insert information through API or different source, for instance:

Retrieve data from a variety of sources and load it into a central repository.

The need for parallelism:

Parallelization is important to import data from multiple sources such as that we used two API datas for analytics or devices simultaneously.

For example:

Simultaneous import of data from the databases of different taxi companies.

Step 2:

Data cleaning

Manage missing, duplicate, and inconsistent values.

The need for parallelism:

Parallel processing is useful for efficient cleaning of large data sets.

For example:

Address NULL values ??in trip_miles to calculate exact distances.

Step 3:

Exchange data

Perform data transformations such as date conversions.
Parallelization speeds up complex transformations on large data sets. For example:
Parallel conversion of timestamp to date object.
Step 4:

Data integration

Consolidate data from various sources into a single data set.
Indispensable for integrating data from multiple sources.
For example:Simultaneously combine taxi trip data from different regions.
Step 5:

Data Enrichment

 

Enhance the dataset with external data sources. The need for parallelism:
Parallel processing simultaneously retrieves and integrates external data.
For example: Added geo coordinates for parallel taxi rides.
Step 6:

Data synthesis

Summarize data or perform aggregations to gain insights.
Accelerate aggregation tasks on large data sets.
For example:Parallel aggregation of daily taxi rides into a monthly summary.
Step 7:

Data serialization

Save the processed data in a structured format.
Useful for recording data segments concurrently.
For example:Store taxi trip data for different years in parallel files.
Step 8:

Check data quality

Data integrity validation by check.
Usually not required but can be used for parallel quality checks.
For example: Also ensure the validity of the timestamp.
Step 9:

Data saving

Store data in data warehouse or cloud storage.
Useful for parallel data transmission.
For example: Also upload taxi ride data to cloud-based storage.
Conclusion

Parallel data management is critical to effectively managing large or complex data sets. This method outlines the steps involved and illustrates why parallel computation is necessary, using the Chicago taxi ride dataset as a practical example. Leveraging parallel processing optimizes data preparation, reduces processing time, and ensures high-quality data is ready for analysis.