How Time Series Databases and Data Lakes Work Together

By Jason Myers / Developer
Feb 19, 2024

In the fast-paced world of software engineering, efficient data management is a cornerstone of success. Imagine you’re working with streams of data that not only require rapid analysis but also need to store that data for long-term insights. This is where the powerful duo of time series databases (TSDBs) and data lakes can help. In this article, we’ll delve into how you can integrate these technologies to create a robust data management framework, providing real-time analytics while ensuring comprehensive historical data analysis.

What is a time series database?

A time series database, like InfluxDB, is a database system optimized for storing and managing time-stamped data. Each record in a TSDB is associated with a timestamp, and these databases efficiently handle the ingestion, processing, and querying of such time-oriented data. TSDBs are used widely in fields like financial services, IoT, environmental monitoring, and more, where sources generate data in a sequential order. Their swift handling of large volumes of such sequential data makes them valuable in scenarios requiring real-time analysis.

What is a data lake?

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It can store data in its raw format and offers vast data processing capabilities. Data lakes are particularly useful for handling massive amounts of diverse data and tasks that require extensive historical data analysis. They provide the flexibility to run different types of analytics, from dashboards and visualizations to big data processing, real-time analytics, and machine learning.

Benefits of using time series databases and data lakes together

While data lakes and data warehouses provide a centralized repository for storing vast amounts of structured and unstructured data, they aren’t optimal for handling high-velocity, time-stamped data. By using a time series database alongside your data lake, you get the following benefits:

• Real-time analysis with long-term storage support - A time series database can provide the speed and efficiency needed for data used for monitoring, anomaly detection, or alerting. You can then offload this data to your data lake for long-term storage, where it can be used for historical analysis and forecasting use cases using cheaper storage options.
• Scalability and flexibility - Both time series databases and data lakes are scalable for their designed purpose, but by using them together, you can cover each one’s weaknesses and gain more flexibility in how your organization handles diverse data types. Your TSDB and data lake can scale up or down depending on current demand and future projected demand.
• Cost savings - TSDBs allow for the efficient ingestion and querying of real-time data, which is crucial for operational monitoring and immediate analytics. However, storing massive amounts of time series data long-term in a TSDB can become costly. By offloading or archiving less frequently accessed data to a data lake, organizations can take advantage of lower storage costs while still maintaining the ability to analyze historical data when needed. This approach optimizes data storage costs and management without compromising the availability or analytical value of the data.

How to integrate time series database and data lakes

While using time series databases and data lakes together has a lot of benefits, the integration can be challenging from a technical perspective. In this section, you will learn about a few common architecture patterns for different use cases.

Hybrid storage architecture

A hybrid storage architecture strikes a balance between the need for real-time analytics and the need for historical data preservation. In this model, raw time series data is initially captured and stored in a TSDB, taking advantage of its optimized performance for real-time analytics and immediate data processing tasks. This setup enables businesses to respond swiftly to time-sensitive information, such as operational anomalies or immediate market trends.

The data is then migrated to a data lake, where it’s stored for long-term analysis. This migration often involves data transformation and downsampling to optimize storage costs and analytical performance in the data lake environment. A hybrid approach is particularly beneficial for scenarios requiring both the high-velocity processing of recent data and historical analysis of accumulated data over time.

Stream processing architecture

A stream processing architecture caters to environments where data velocity and real-time processing are paramount. Upon ingestion into a TSDB, data flows through a streaming engine, such as Kafka, Telegraf, or Flink, facilitating a continuous and dynamic data pipeline. This engine not only transports data between the TSDB and the data lake but also provides capabilities for real-time data manipulation, filtering, and aggregation.

This architecture excels in scenarios with stringent requirements for real-time data analysis, alerting, and decision-making while simultaneously archiving data in a data lake for historical analytics and regulatory compliance. It’s an ideal fit for IoT ecosystems, financial trading platforms, and real-time monitoring systems, where the value of data is heavily time-dependent.

Data lakehouse architecture

The data lakehouse architecture represents a convergence of data lakes and data warehouses, aiming to offer the best of both worlds. By integrating a TSDB within a data lakehouse architecture, organizations can achieve real-time operational analytics while benefiting from the structured and unstructured data storage capabilities of a data lake. This setup supports a broad range of analytical workloads, from machine learning models requiring vast datasets to BI tools requiring structured data.

The TSDB’s role in this architecture is to provide a layer that specializes in handling time-sensitive data, enabling immediate analytics and operational intelligence. Meanwhile, the data lakehouse, with enhanced data management features such as ACID transactions and schema enforcement, ensures that the data remains reliable, consistent, and readily available for complex queries and long-term strategic analysis.

Next steps

Integrating a time series database with data lakes provides a comprehensive data management solution that caters to real-time and historical data needs. For software engineers, this integration means more efficient data processing, better resource management, and a holistic approach to data analytics. As a next step, exploring and testing out a solution like InfluxDB in conjunction with your data lake solutions can be a game-changer in how you handle and derive insights from your data streams.