What are the best practices for storing and retrieving big data?

2 Distributed systems

Distributed systems are systems that consist of multiple nodes or machines that communicate and coordinate with each other to perform a common task. Distributed systems are essential for big data, as they enable you to store and process large volumes of data across multiple servers, rather than relying on a single point of failure. Distributed systems can also provide benefits such as fault tolerance, load balancing, parallelism, scalability, and availability. However, distributed systems also introduce challenges such as network latency, concurrency, consistency, security, and complexity. You should use distributed systems that are designed for big data, such as Hadoop, Spark, Cassandra, MongoDB, and others. You should also follow the principles and patterns of distributed systems, such as sharding, replication, partitioning, caching, batching, and others.

3 Data compression

Data compression is the process of reducing the size of your data by applying algorithms that eliminate or encode redundant or irrelevant information. Data compression can help you save storage space, bandwidth, and cost for your big data. It can also improve the performance of your data processing and retrieval, as it reduces the amount of data that needs to be read and transferred. However, data compression also has some drawbacks, such as increased CPU usage, reduced data quality, and potential data loss. You should use data compression techniques that are suitable for your data type, format, and purpose. For example, you can use lossless compression for text or numeric data, and lossy compression for images or audio data. You can also use different compression levels, formats, and tools, such as gzip, bzip2, snappy, lz4, and others.

4 Query optimization

Query optimization is the process of improving the efficiency and effectiveness of your queries that retrieve data from your database. Query optimization can help you reduce the execution time, resource consumption, and cost of your queries, as well as improve the accuracy and relevance of your results. Query optimization involves various techniques and strategies, such as indexing, filtering, sorting, aggregating, joining, partitioning, caching, and others. You should use query optimization methods that are appropriate for your data model, system, and use cases. For example, you can use indexes to speed up your searches, but you should also avoid creating too many or unnecessary indexes, as they can slow down your updates and inserts. You can also use filters to limit your results, but you should also avoid using complex or nested filters, as they can increase your processing time. You can also use joins to combine data from different sources, but you should also avoid using cross joins or cartesian products, as they can generate huge and redundant results.

5 Data quality

Data quality is the measure of how accurate, complete, consistent, and reliable your data is. Data quality is crucial for big data, as it affects the validity and value of your data analysis and decision making. Poor data quality can lead to inaccurate results, misleading insights, and wrong actions. Data quality can be affected by various factors, such as data sources, formats, collection methods, integration processes, and cleaning techniques. You should ensure data quality by following the best practices of data governance, such as defining data standards, policies, and roles, implementing data validation, verification, and correction mechanisms, monitoring data quality metrics and indicators, and resolving data quality issues and errors.

6 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?