Improve: findings from automatic upload

Author: danieldanielecki

README.md

@@ -29,6 +29,7 @@
    - Amazon Simple Storage Service (Amazon S3);
    - Amazon Textract;
    - Amazon Transcribe;
+   - Apache Parquet;
    - Apache Spark;
    - AWS Batch;
    - AWS Glue;
@@ -833,10 +834,8 @@ We are so thankful for every contribution, which makes sure we can deliver top-n
 ### A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data. The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards. Which solution should the Data Scientist build to satisfy the requirements?
 
 - [x] Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS GlueData Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
-- [ ] Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache
-Parquet or ORC format and writes the data to a processed data location inAmazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
-- [ ] Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache
-Parquet or ORC format and inserts it into an Amazon RDS PostgreSQLdatabase. Have the Analysts query and run dashboards from the RDS database.
+- [ ] Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location inAmazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
+- [ ] Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQLdatabase. Have the Analysts query and run dashboards from the RDS database.
 - [ ] Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3using Amazon Athena and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
 
 ### A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as Linear Regression and Logistic Regression. During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. This may make the model unstable. What should be done to reduce the impact of having such a large number of features?