Cloud1 is a leader in ETL and Data Integration solutions. Our ETL solutions are for data at rest as well as data in motion. Data at rest ETL solutions are ready to use on-demand deployable, resulting in huge savings. Data in motion ETL solutions are a class in itself, addressing issues with dynamically streaming data using Kafka/Kinesis. Cloud1’ ISO 9001 based Quality Assurance delivers solutions with highest quality all the time. Cloud1’ ISO 27001 based information security makes sure that your data is protected both at rest as well as in motion.

Cloud1 ETL Service offerings

ETL (or Extract, Transform, Load) enables your business to integrate multiple data sources, enrich and clean data, as well as migrate data between data sources. ETL is a fundamental capability and is a necessity for your business. The three words in Extract Transform Load (ETL) each describe a process in the moving of data from its source to a formal data storage system.

Extract:

Data is extracted from the source system into the staging area. Transformations, if any, are done in the staging area so that the performance of source system in not degraded. Also, if corrupted data is copied directly from the source into Data warehouse database, rollback will be a challenge. Staging area gives an opportunity to validate extracted data before it moves into the Data warehouse.

Transform:

Data extracted from source server is raw and not usable in its original form. Therefore it needs to be cleansed, mapped and transformed. Transformation helps you perform customized operations on data. For instance, if the user wants Sum-of-Annual revenue which is not in the database, or, if the first name and the last name in a table is in different columns, it is possible to link them before loading.

Load:

Loading data into the target data warehouse database is the last step of the ETL process. In a typical Data warehouse, a huge volume of data needs to be loaded in a relatively short period.

Cloud1 has experience developing, operating and maintaining both batch and streaming ETL in the cloud. Our preferred approaches to ETL are:

● For streaming ETL, we prefer Kafka (or AWS Kinesis) combined with microservices.
● For complex batch ETL, we prefer Airflow plus AWS EMR (Spark).

Case Study

Cloud1 develops and operates a combination of streaming ETL (Kinesis and Kafka) and batch ETL (Airflow plus Spark) for a leading Cyber Security company. Our ETL solution enables the customer to combine disparate data sources into a data lake and then migrate the analysis results from the data lake into query-optimized databases in real-time. The key business value delivered by Cloud1 is that our customer is able to rapidly identify more security threats.