SQL struggles to manage huge amounts of time series data owing to relational databases’ poor design. These databases are not intended for large-volume analytical queries. SQL is most commonly associated with transactional relational database management systems (RDBMS), which retain and update records according to a strict structure. As a result, many engineers and data analysts are already conversant with SQL.
Given its unstructured character and the requirement for a unique database, time series data poses significant issues for relational databases. To manage enormous volumes of semi-structured or unstructured data from edge devices, developers are migrating to NoSQL and time series databases. Traditional SQL databases are unsuitable for managing time series data, but adopting a purpose-built time series database provides a lifeline for developers.
Time series data is, by nature, non-relational
Time series data is rapidly being utilised for real-time analytics to spot trends and patterns, empowering developers and tech executives to make quick and educated decisions. Users must think in terms of time and establish a time window for their queries. However, because of the time and resources necessary to query related data from several databases, relational data might be more difficult.
The capacity to scale is important
As internet connectivity expands, so does the amount of data created by devices. Due to data input difficulties and latency, transactional databases fail to grow. A time series database with SQL support can deliver massive data sets with scalability and speed, allowing for the continuous import, transformation, and analysis of billions of data points per second. A scalable database is essential for managing time series data as its quantities expand. High compression lowers storage costs and enables up to ten times more storage without affecting performance.
Time series can be queried using SQL
A purpose-made time series database created with Apache DataFusion, a distributed SQL query engine, outperforms others. The Apache Arrow ecosystem contains DataFusion, the Flight SQL query engine, and Apache Parquet. Without transforming data to Arrow format, Flight SQL provides a high-performance SQL interface for quicker data access and lower latencies. To promote simple access between Flight SQL and clients written in Go, a FlightSQL driver, a lightweight wrapper, is constructed.
The Apache Arrow ecosystem is appropriate for time series data since it employs columnar formats for in-memory storage and durable file formats. This enables SIMD instructions to be used for low-cost compression, high-cardinality use cases, and quicker scan rates. In contrast to row-oriented storage, which involves searching for every field, tag set, and timestamp, users may discover the maximum value of a field in the first column of data. Unlike row-oriented storage, Apache Arrow provides a quicker and more efficient mechanism for querying and publishing time series data.
There are several advantages of using a language-independent software architecture
Apache Arrow is a language-independent framework that allows developers to deal with data closer to its source, minimzing the need for ETL operations and making big data sets easier to manage. It works with multiple data processing libraries and native libraries in various programming languages, guaranteeing that all systems utilise the same memory format, that there is no overhead in cross-system communication, and that data interchange is standard and interoperable.
The peak season for time series
Time series data, which includes events, clicks, sensor data, logs, metrics, and traces, provides a wealth of information for real-time analytics, predictive analysis, IoT monitoring, app monitoring, and DevOps monitoring. It is a necessary tool for making data-driven decisions. SQL-based queries can help developers with RDBMS knowledge overcome the gap between transactional and analytical workloads.
A SQL-supported time series database on Apache Arrow improves interoperability by qualifying developers to manage massive amounts of data while also using visual and analytical tools.
Conclusion
The implementation of SQL into time series data processing not only combines the best of both worlds but also lays the groundwork for the future of data analysis practices, bringing us one step closer to completely realising the value of all the data around us.
