How can you choose the right data storage and processing platform?

1 Data types and sources

The first factor to consider is the type and source of your data. Is it structured, semi-structured, or unstructured? Is it streaming or batch? Is it from a single source or multiple sources? These questions will help you narrow down the possible data storage and processing platforms that can handle your data format and ingestion. For example, if you have structured data from a single source, you might choose a relational database as your data storage and a SQL-based tool as your data processing platform. If you have unstructured data from multiple sources, you might choose a distributed file system as your data storage and a big data framework as your data processing platform.

2 Data volume and velocity

The second factor to consider is the volume and velocity of your data. How much data do you have and how fast does it grow and change? These questions will help you determine the scalability and performance requirements of your data storage and processing platform. For example, if you have a large amount of data that grows rapidly and changes frequently, you might need a data storage and processing platform that can scale horizontally, distribute the workload, and handle real-time or near-real-time processing. If you have a small amount of data that grows slowly and changes infrequently, you might need a data storage and processing platform that can scale vertically, optimize the queries, and handle batch processing.

3 Data quality and governance

The third factor to consider is the quality and governance of your data. How accurate, consistent, and complete is your data? How do you ensure the security, privacy, and compliance of your data? These questions will help you assess the reliability and trustworthiness of your data storage and processing platform. For example, if you have high-quality data that needs strict security, privacy, and compliance measures, you might need a data storage and processing platform that can provide data validation, cleansing, encryption, auditing, and access control. If you have low-quality data that needs flexible security, privacy, and compliance measures, you might need a data storage and processing platform that can provide data transformation, enrichment, anonymization, logging, and authorization.

4 Data analysis and usage

The fourth factor to consider is the analysis and usage of your data. How do you analyze, visualize, and communicate your data insights? How do you use your data to support your business goals and decisions? These questions will help you identify the functionality and usability of your data storage and processing platform. For example, if you need to perform complex, advanced, and interactive data analysis, you might need a data storage and processing platform that can support various analytical tools, languages, and frameworks. If you need to perform simple, basic, and static data analysis, you might need a data storage and processing platform that can support standard reporting and dashboarding tools.

5 Data cost and value

The fifth factor to consider is the cost and value of your data. How much does it cost to store, process, and analyze your data? How much value does your data generate for your organization and stakeholders? These questions will help you evaluate the return on investment (ROI) of your data storage and processing platform. For example, if you have a high-cost and low-value data, you might need a data storage and processing platform that can reduce the storage, processing, and analysis costs and increase the data value. If you have a low-cost and high-value data, you might need a data storage and processing platform that can maximize the storage, processing, and analysis efficiency and effectiveness.

6 Data storage and processing platform examples

The final factor to consider is the availability and suitability of different data storage and processing platform examples. What are some of the common and popular data storage and processing platforms in the market? How do they compare and contrast in terms of the factors mentioned above? These questions will help you make an informed and confident decision when choosing a data storage and processing platform. For example, some of the common and popular data storage platforms are MySQL, MongoDB, Hadoop, and Amazon S3. Some of the common and popular data processing platforms are Spark, Hive, Tableau, and Power BI.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?