What's the best way to pick data storage and processing tech?

1 Data volume and velocity

One of the first factors to consider is the volume and velocity of your data. How much data do you have to store and process, and how fast does it grow and change? This will affect the type and size of storage and processing resources you need, as well as the scalability and performance requirements. For example, if you have a large amount of streaming data that needs to be processed in real time, you might need a distributed storage system that can handle high throughput and low latency, such as Apache Kafka or Amazon Kinesis. On the other hand, if you have a small amount of static data that does not change frequently, you might be fine with a simple relational database or a file system.

2 Data variety and quality

Another factor to consider is the variety and quality of your data. How diverse and complex are the sources, formats, and structures of your data? How consistent and accurate is your data? This will affect the level and type of processing and integration you need, as well as the data governance and quality assurance practices. For example, if you have a lot of unstructured or semi-structured data from different sources, such as text, images, or web logs, you might need a flexible and scalable processing framework that can handle various data types and schemas, such as Apache Spark or Apache Hadoop. On the other hand, if you have a lot of structured and standardized data from a single source, such as a transactional system or a CRM, you might be able to use a simpler and faster processing tool, such as SQL or Python.

3 Data access and usage

A third factor to consider is the access and usage of your data. How do you and your users want to access and use your data? What are the frequency, purpose, and mode of data access and usage? This will affect the security, availability, and usability of your storage and processing systems, as well as the data delivery and presentation methods. For example, if you and your users need to access and use your data frequently and interactively, such as for ad hoc queries or dashboards, you might need a storage system that can support fast and easy data retrieval, such as a data warehouse or a data lake. On the other hand, if you and your users need to access and use your data infrequently and batch-wise, such as for reporting or analytics, you might be able to use a storage system that can support large and complex data processing, such as a data mart or a data pipeline.

4 Data cost and value

A final factor to consider is the cost and value of your data. How much does it cost to store and process your data, and how much value does it generate for your business or organization? This will affect the trade-offs and priorities you have to make when choosing storage and processing technologies, as well as the return on investment and the data lifecycle management. For example, if your data has a high cost and a low value, such as historical or archival data that is rarely used or accessed, you might want to use a storage system that can reduce the cost of data storage, such as a cloud storage service or a data lake. On the other hand, if your data has a low cost and a high value, such as operational or strategic data that is frequently used or accessed, you might want to use a storage system that can enhance the value of data usage, such as a data warehouse or a data mart.

Choosing the best data storage and processing tech is not a one-size-fits-all solution. It depends on the characteristics and needs of your data and your business or organization. By considering these four factors and asking yourself some relevant questions, you can narrow down your options and make informed decisions that suit your data engineering project.