Client purpose
Volotea is a low-cost Spanish airline founded in 2011. It reaches over 100 airports and has bases in 19 European mid-size capitals. Volotea operates approximately 70,000 flights per year.
The company took the decision to migrate their cloud form Azure to AWS. Volotea was facing exponential growth in data and sources, resulting in a complex and costly data architecture to maintain. With multiple systems generating data independently, there was no unified view or governance over the information. In addition, infrastructure costs skyrocketed due to unnecessary data replication and inefficient processes.
To solve these issues, the following was proposed:
- Redesign the data architecture with a focus on organizing information flows, reducing duplications, and improving processes.
- Implement a cloud data warehouse using serverless technologies to reduce costs.
- Centralize all data in a data lake and eliminating silos.
- Create ETL processes to gain control and traceability over data manipulation.
Since the company had already decided to migrate clouds from Azure to AWS, we took advantage of the migration to also restructure the data architecture in the new cloud.
How we navigate it
We analysed their systems and tools. Based on our findings, we designed a new cloud-based data architecture based on Amazon S3 for storage, AWS Glue for ETL, and Amazon Redshift Serverless for the data warehouse. Data flows through a series of stages from different sources into the data warehouse, which is composed of external Delta Tables in S3. This allows Redshift Serverless to leverage S3 data using Spectrum.
The data in the data warehouse can then be consumed by users, BI tools, and other applications like machine learning models. Metadata tables provide flexibility, understanding, and ease of maintenance for the data pipelines.
Key steps in the data flow are:
- Extraction from data sources into Parquet files in an S3 landing bucket.
- Structuring, typing, and cleaning data in the staging area.
- Loading transformed data into consumption tables and views.
- Orchestrating services and automating processes with AWS Step Functions.
Where it took us
With this new architecture, the company achieved:
- A unified view of information for improved analysis and decision making.
- Significantly reduced infrastructure costs.
- Easy scaling as data volumes grows.
In summary, the restructuring of the data architecture is proving to be a success, delivering sustainable value to the business. We are currently extending the process to other areas of the business.
Client purpose
Volotea is a low-cost Spanish airline founded in 2011. It reaches over 100 airports and has bases in 19 European mid-size capitals. Volotea operates approximately 70,000 flights per year.
The company took the decision to migrate their cloud form Azure to AWS. Volotea was facing exponential growth in data and sources, resulting in a complex and costly data architecture to maintain. With multiple systems generating data independently, there was no unified view or governance over the information. In addition, infrastructure costs skyrocketed due to unnecessary data replication and inefficient processes.
To solve these issues, the following was proposed:
- Redesign the data architecture with a focus on organizing information flows, reducing duplications, and improving processes.
- Implement a cloud data warehouse using serverless technologies to reduce costs.
- Centralize all data in a data lake and eliminating silos.
- Create ETL processes to gain control and traceability over data manipulation.
Since the company had already decided to migrate clouds from Azure to AWS, we took advantage of the migration to also restructure the data architecture in the new cloud.
How we navigate it
We analysed their systems and tools. Based on our findings, we designed a new cloud-based data architecture based on Amazon S3 for storage, AWS Glue for ETL, and Amazon Redshift Serverless for the data warehouse. Data flows through a series of stages from different sources into the data warehouse, which is composed of external Delta Tables in S3. This allows Redshift Serverless to leverage S3 data using Spectrum.
The data in the data warehouse can then be consumed by users, BI tools, and other applications like machine learning models. Metadata tables provide flexibility, understanding, and ease of maintenance for the data pipelines.
Key steps in the data flow are:
- Extraction from data sources into Parquet files in an S3 landing bucket.
- Structuring, typing, and cleaning data in the staging area.
- Loading transformed data into consumption tables and views.
- Orchestrating services and automating processes with AWS Step Functions.
How to change an entire data strategy
I imagine 8wires as that sincere and honest partner that takes you out of all that noise and helps you focus on what is important, no matter how unsexy it may be, to achieve a great long-term goal.
The one who accompanies you through the hard times and helps you through the tough decisions, knowing that there is no easy road. The one who gives you the push or the tools so that you climb and be yourself the one who reaches the summits you propose in a healthy, sustainable and energetic way. And above all, the one who steps aside when he knows that he is not helping you or that he will not be able to give you what you need.
I don't know if it is helpful, but somehow I saw on the web a visual explanation of the problem in the data/technology world (I don't know if with this metaphor) before showing how it is to work with us and finally, another visual explanation of the result.
Like a bridge over troubled waters
I imagine 8wires as that sincere and honest partner that takes you out of all that noise and helps you focus on what is important, no matter how unsexy it may be, to achieve a great long-term goal.
The one who accompanies you through the hard times and helps you through the tough decisions, knowing that there is no easy road. The one who gives you the push or the tools so that you climb and be yourself the one who reaches the summits you propose in a healthy, sustainable and energetic way.
Client purpose
Volotea is a low-cost Spanish airline founded in 2011. It reaches over 100 airports and has bases in 19 European mid-size capitals. Volotea operates approximately 70,000 flights per year.
The company took the decision to migrate their cloud form Azure to AWS. Volotea was facing exponential growth in data and sources, resulting in a complex and costly data architecture to maintain. With multiple systems generating data independently, there was no unified view or governance over the information. In addition, infrastructure costs skyrocketed due to unnecessary data replication and inefficient processes.
To solve these issues, the following was proposed:
- Redesign the data architecture with a focus on organizing information flows, reducing duplications, and improving processes.
- Implement a cloud data warehouse using serverless technologies to reduce costs.
- Centralize all data in a data lake and eliminating silos.
- Create ETL processes to gain control and traceability over data manipulation.
Since the company had already decided to migrate clouds from Azure to AWS, we took advantage of the migration to also restructure the data architecture in the new cloud.
How we navigate it
We analysed their systems and tools. Based on our findings, we designed a new cloud-based data architecture based on Amazon S3 for storage, AWS Glue for ETL, and Amazon Redshift Serverless for the data warehouse. Data flows through a series of stages from different sources into the data warehouse, which is composed of external Delta Tables in S3. This allows Redshift Serverless to leverage S3 data using Spectrum.
The data in the data warehouse can then be consumed by users, BI tools, and other applications like machine learning models. Metadata tables provide flexibility, understanding, and ease of maintenance for the data pipelines.
Key steps in the data flow are:
- Extraction from data sources into Parquet files in an S3 landing bucket.
- Structuring, typing, and cleaning data in the staging area.
- Loading transformed data into consumption tables and views.
- Orchestrating services and automating processes with AWS Step Functions.
How to change an entire data strategy
I imagine 8wires as that sincere and honest partner that takes you out of all that noise and helps you focus on what is important, no matter how unsexy it may be, to achieve a great long-term goal.
The one who accompanies you through the hard times and helps you through the tough decisions, knowing that there is no easy road. The one who gives you the push or the tools so that you climb and be yourself the one who reaches the summits you propose in a healthy, sustainable and energetic way. And above all, the one who steps aside when he knows that he is not helping you or that he will not be able to give you what you need.
I don't know if it is helpful, but somehow I saw on the web a visual explanation of the problem in the data/technology world (I don't know if with this metaphor) before showing how it is to work with us and finally, another visual explanation of the result.
Like a bridge over troubled waters
I imagine 8wires as that sincere and honest partner that takes you out of all that noise and helps you focus on what is important, no matter how unsexy it may be, to achieve a great long-term goal.
The one who accompanies you through the hard times and helps you through the tough decisions, knowing that there is no easy road. The one who gives you the push or the tools so that you climb and be yourself the one who reaches the summits you propose in a healthy, sustainable and energetic way.
