Time-series data usually refers to any series of data points that come with a timestamp. You’ll find time-series data created by a wide range of sources including sensors on vehicles, weather stations, package tracking, fitness trackers and so on. Other sources of time-series data include financial market data, website traffic, server logs and more.
By studying time-series data, you can gain insights into how things change and behave over time, which can help you make predictions or better understand the past.
But time-series data can pile up fast, and it’s often messy. How do you make sense of it all? What systems do you need to store, analyze and develop insights and understanding from that data.
What is different about time-series data?
Time-series data has some unique characteristics that require a bit more thought when selecting systems that can work with it:
- High Velocity:
Time-series data can accumulate rapidly. Many businesses monitor hundreds of thousands, or millions of sensors outputting data points every minute, or even every second. Ingesting and storing such data requires efficient data ingestion mechanisms. - High Cardinality:
Cardinality refers to the uniqueness of the data that will be indexed. If you only have ten products and ten orders, your data has low cardinality. But time-series data typically has high cardinality with unique timestamps, unique locations, and unique values. Analyzing such high-cardinality time-series data can be computationally intensive. - High Volume:
Time-series data can quickly add up. It’s important to have systems that can scale horizontally and vertically to handle increasing data sizes and processing demands.
Can you use a relational database for time-series data?
Yes. You can store and work with time-series data in most common relational databases such as MySQL, Postgres or SQL Server. But relational databases are typically optimized for more general use cases and may not be a good fit for analyzing your time-series data.
Factors to consider include:
- Data model:
Relational databases use a tabular data model with predefined schemas consisting of tables, rows, and columns. Time-series data is better organized around timestamps and values, often using a key-value pair or a columnar structure. - Query Speed:
Time-series databases are designed for efficient time-based querying, allowing for fast retrieval of data within specific time ranges. They typically provide functions and optimizations specifically tailored for time-series analysis, such as downsampling, aggregation, and windowing operations. Relational databases are flexible in terms of querying capabilities and support a wide range of complex relational queries involving multiple tables and joins. - Write performance:
Time-series databases need to be optimized for high write throughput, enabling efficient storage of large volumes of time-series data. They should handle frequent data inserts and updates, often at high speeds. Relational databases typically have slightly lower write performance due to the additional overhead associated with enforcing relational constraints and maintaining indexes. - Storage efficiency:
Time-series data often requires compression techniques and data downsampling to optimize storage efficiency. Databases need to handle large volumes of data while minimizing storage requirements. Relational databases typically store data in a more structured format, which may result in less efficient storage for time-series data. - Scalability:
Time-series systems should be built with scalability in mind, allowing for horizontal scaling across multiple nodes or clusters. They should handle the increasing data volumes and performance demands of time-series applications. - Schema flexibility:
Relational databases enforce strict schemas and data types, requiring upfront schema design and defined relationships between tables. Time-series databases have more flexibility in terms of schema design, often allowing for schema-on-read or schemaless approaches.
Addressing the specific challenges posed by the storage and analysis of vast amounts of time-stamped data usually requires a more specialized time-series database.
Kinetica for Time-Series Data
Kinetica is a high-performance analytical database that is well suited to working with time-series data. One of it’s most stand-out characteristics is how it uses vectorized query algorithms to perform complex high-cardinality joins with ease.
High cardinality joins are where there are numerous unique values in the join columns, such as timestamps or device identifiers. For example, you might need to find all events which happened during a time window, near a certain location. Every time and location in the database has a unique value, and that is hard for other databases to index and join across.
Kinetica’s unique vectorized architecture takes advantage of advances in modern processors to more efficiently perform complex high-cardinality joins that other systems would struggle to complete.
This capability is particularly valuable in large time-series data scenarios, where the volume and complexity of data can be overwhelming. Kinetica is able to combine time-series data with other relevant information, such as contextual data or metadata, allowing for deeper analysis and more accurate correlations. In turn this enables organizations to uncover hidden patterns, detect anomalies, and make data-driven decisions based on a holistic view of their data.
Other notable features of Kinetica include:
- High-performance Ingest:
With time-series data, the speed at which data is ingested becomes critical. Kinetica can provide efficient and fast ingestion by distributing the process across multiple nodes, and reducing reliance on a head node. This makes Kinetica adept at handling millions or even billions of data points per second. They employ optimized storage formats and data structures to minimize disk I/O and leverage techniques like data compression and indexing to accelerate data ingestion. - Querying and analysis capabilities:
Kinetica provides sophisticated querying and analysis capabilities suitable for time-series data. Native support is available for time-series-specific operations such as time-range selection, aggregation, and filtering. Kinetica also provides advanced analytics functions, including geospatial functions, complex event processing, and machine learning algorithms to uncover patterns and insights in time-series data. You can even bring your own machine learning algorithms and statistical functions directly into the database for detecting patterns, trends, and anomalies in time-series data. These capabilities enable proactive monitoring, predictive maintenance, and real-time anomaly detection for various domains, such as finance, IoT, cybersecurity, and industrial monitoring. - Horizontal Scalability
Traditional relational databases are poorly equipped to handle the high volume, velocity, and variety of time-series data generated by big data applications. Kinetica is able to scale horizontally, allowing it to handle massive data ingestion rates and storage requirements. Kinetica leverages distributed architectures and clustering techniques to distribute data across multiple nodes and ensure high performance and availability. - Integration with big data ecosystems:
Kinetica is adapted to play well with time-series ecosystems and tools. Kinetica provides connectors and APIs for streaming ingest from Kafka and other stream processors, enabling efficient data transfer and analysis across the entire data pipeline. Integration with streaming platforms such as Apache Kafka allows real-time processing of incoming time-series data, enabling instant insights and actions. - Ease of use
There’s a wide ecosystem of tools available for working with time-series data. Kinetica makes it easy to directly connect observability tools, such as Grafana and Tableau, via a Postgres wireline protocol. Kinetica also comes with a highly featured and powerful notebook interface that makes it easy to set up step by step analysis of data simply using queries and in-built visualization tools. - Compression and storage optimization:
Storing large volumes of time-series data can be expensive. Kinetica utilizes advanced storage strategies such as columnar storage and tiered storage from in-memory to disk to optimize data query and minimize disk space usage. Kinetica can also retrieve historical data from low-cost cloud object storage.
Overall, Kinetica provides a powerful, yet easy to use platform for working with time-series data. It offers scalability, performance, storage optimization, advanced analytics, and seamless integration with the broader data ecosystem. These features empower organizations to more effectively manage and analyze large-scale time-series data, unlocking valuable insights and enabling data-driven decision-making.
You can try Kinetica yourself, quickly and easily, with Kinetica Cloud. Kinetica is free to use for developers and datasets under 10GB. If you’d like, book a demo, and we’ll be delighted to show you how it works, and help you figure out how to solve your business challenges.
