Low-Latency Data Sync in Hybrid Architectures

Hybrid edge-cloud systems are becoming essential as applications demand real-time performance. These architectures combine edge devices for immediate tasks and cloud systems for scalability. But synchronizing data between them while maintaining low latency is challenging. Key hurdles include network delays, data consistency, scalability issues, bandwidth constraints, and handling concurrent updates.

Key Takeaways:

• Challenges: Synchronization faces delays, conflicts, and scalability problems across hybrid systems.

• Solutions: Techniques like Change Data Capture (CDC), data compression, asynchronous sync, and edge caching help reduce latency and improve performance.

• Tools: Platforms like Tinybird simplify hybrid setups, while ClickHouse® offers more control for advanced users.

• Comparison: Tinybird is quick to deploy, ClickHouse Cloud balances control and ease, and self-managed ClickHouse® suits teams with in-depth expertise.

Hybrid systems require balancing speed, consistency, and cost. Choosing the right tools and strategies ensures smooth, low-latency synchronization for edge-cloud environments.

Hybrid Cloud File Sharing Using Azure File Sync | Azure File Sync with AWS

Methods for Low-Latency Data Synchronization

Achieving low-latency data synchronization in hybrid systems requires approaches that cut down on data transfer overhead while keeping everything consistent. Let’s break down some proven methods to reduce delays and improve overall performance.

Change Data Capture (CDC) for Faster Sync

Change Data Capture (CDC) focuses on transmitting only the changes in data rather than transferring entire datasets. It tracks specific updates, inserts, and deletes in the source system, making synchronization faster and more efficient.

"Change Data Capture (CDC) is a technique used to detect and record changes such as inserts, updates, and deletes in a database. CDC improves data efficiency by capturing only changed records, making it essential for real-time data replication, ETL pipelines, and syncing data across systems."

• Kevin Bartley [1]

This approach is a game-changer for businesses. A staggering 58% of companies struggle with slow or poor-quality data access, which hampers decision-making [1]. By focusing on incremental updates, CDC reduces the load on systems compared to full data transfers.

There are several ways to implement CDC, depending on your system's needs:

CDC is widely used in real-world scenarios. For instance, retailers use it to track inventory changes and update product catalogs in real time. Similarly, manufacturers rely on it to sync production data with inventory systems, ensuring smooth operations [1].

When adopting CDC, start by assessing your data integration needs - like source systems, update frequency, and latency requirements. This will help you pick the right CDC method for your use case [1].

Data Compression and Bundling Techniques

Data compression is another way to tackle bandwidth limitations in hybrid systems. By reducing the size of transmitted data, compression helps optimize data transfer and minimize delays.

Even a one-second delay can reduce conversion rates by 2.11%, so every millisecond counts [4].

Compression techniques generally fall into two categories:

• Lossless Compression: Reduces file size without losing any data, allowing for full restoration.

• Lossy Compression: Achieves greater size reduction by permanently removing some data, which may slightly impact quality [3].

"Lossy will save you the most space, but can affect your image quality. Lossless saves less space, but won't usually impact your image quality."

• Financial IT [3]

Another effective method is delta synchronization, where only the differences (or deltas) between dataset versions are sent. This keeps data transfers lean and fast [2].

To maximize efficiency, compress large datasets before transferring them. With 90% of the world’s data generated in just the past two years, this step has become increasingly critical [4].

Asynchronous Sync and Edge Caching

Asynchronous synchronization allows systems to update data in the background without disrupting operations. This method prioritizes responsiveness, tolerating temporary inconsistencies for the sake of performance.

When paired with edge caching, asynchronous sync becomes even more effective. Edge caching stores frequently accessed data locally, giving users instant access while background sync keeps everything up-to-date [5].

Together, these techniques reduce latency and ease bandwidth demands. Edge caching ensures users experience minimal delay [6], while asynchronous sync guarantees eventual consistency across the system.

To get the best results, tailor your synchronization strategy to match your system’s specific performance and consistency needs. Adaptive methods can adjust update frequency based on network conditions, ensuring a smooth balance between speed and accuracy [5].

Tinybird vs. ClickHouse® for Hybrid Architectures

When designing hybrid edge-cloud systems that require low-latency data synchronization, selecting the right OLAP solution is a crucial step. While ClickHouse® delivers the performance needed for real-time analytics, deciding between self-managed ClickHouse®, ClickHouse Cloud, and Tinybird often boils down to balancing development speed with control. These differences provide a framework for evaluating how to scale ClickHouse® effectively in hybrid environments.

Tinybird's Low-Latency Sync Features

Tinybird simplifies the complexities of ClickHouse® while maintaining its performance edge. Designed for developers who need to move quickly, Tinybird offers features that make low-latency data synchronization in hybrid setups much easier.

Streaming ingestion lies at the heart of Tinybird's platform. With the Events API, you can stream JSON/NDJSON events directly from your application using simple HTTP requests [9]. This eliminates the need for complex ingestion pipelines, making edge synchronization faster and more efficient.

Materialized views in Tinybird are automatically refreshed, which significantly speeds up queries and reduces the number of rows scanned. In fact, these views can make queries up to 50 times faster while scanning as many as 50,000 times fewer rows [8].

Another standout feature is Tinybird's built-in API generation. With just a single click, you can turn SQL queries into REST endpoints. This is particularly useful in hybrid architectures where edge applications rely on rapid API access to synchronized data. Tinybird supports over 50 billion API requests annually and auto-generates OpenAPI specifications for easy integration [7].

Performance metrics highlight the platform's capabilities. Many Tinybird users report write speeds exceeding 20 MB/s and over 1,000 queries per second, all with a p95 query latency of under 50ms right out of the box [7]. On top of that, 93% of non-enterprise Tinybird customers on paid plans spend less than $100 per month [7].

"Without Tinybird, we would have needed people to set up and maintain ClickHouse, people to manage the API layer, people to manage the ETLs. Tinybird has easily saved us from having to hire like 3 to 5 more engineers."

• Javier Baena, Head of Data Platform at Audiense [11]

Feature Comparison Table

Here’s a breakdown of how Tinybird, ClickHouse Cloud, and self-managed ClickHouse® compare for hybrid deployments:

For hybrid setups, Tinybird also provides managed connectors for services like DynamoDB, S3, GCS, and Kafka - options not available with ClickHouse Cloud [7].

The operational differences are striking:

"With Tinybird, we don't have to worry about scaling a database. We don't have to worry about spikes in traffic. We don't have to worry about managing ingestion or API layers. We just build and let Tinybird worry about it."

• Steven Tey, Founder and CEO at Dub [11]

That said, if your team has deep ClickHouse® expertise and needs granular control over database configurations, ClickHouse Cloud or a self-managed solution may be a better fit. Tinybird is geared toward accelerating development, while ClickHouse® options cater to those who prioritize customization [7].

Pricing structures also vary. ClickHouse Cloud development plans start at $1 to $193 per month, with production plans beginning at $500 [10]. Tinybird, on the other hand, offers a straightforward usage-based model: $0.34 per GB compressed per month and $0.07 per GB of processed data [10].

This comparison highlights the trade-offs, helping you choose the best option for achieving optimal hybrid sync performance. For teams that prioritize control, the next section explores strategies to enhance ClickHouse® performance in hybrid environments.

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How to Run ClickHouse® at Scale in Hybrid Setups

Running ClickHouse® in hybrid environments requires a strong focus on network architecture, smart schema design, and consistent maintenance. Hybrid setups, by nature, introduce complexity, making it essential to approach these areas with precision from the beginning. Let’s break it down.

Network Setup and Configuration

The network setup is the backbone of any hybrid ClickHouse® deployment. To ensure high availability, aim for three replicas per shard. This setup not only safeguards against node failures but also helps maintain query performance across your hybrid infrastructure[13].

For reliability, distribute replicas across different availability zones, keeping the round-trip latency at or below 20 ms[13]. Within each zone, place servers on separate physical hardware to eliminate single points of failure.

When it comes to coordination, use three ZooKeeper replicas to avoid losing quorum, or consider deploying ClickHouse Keeper in an odd-numbered ensemble for similar functionality[13][14].

For secure, high-speed connections, rely on private links and 10 GbE+ interfaces with TLS/SSL encryption. ClickHouse Cloud even offers cross-region PrivateLink in beta, which can simplify complex deployments[12][14].

Load balancing is another key component. It helps distribute query loads evenly across nodes. Services like NetApp Instaclustr make this process straightforward - sometimes as simple as checking a box during setup[14].

Once your network is in place, the next step is designing an efficient schema.

Schema Design for Distributed Writes

Schema design plays a pivotal role in hybrid environments, especially for managing data flows between edge and cloud components. Poor schema choices can lead to higher synchronization latency, so it's important to get this right.

Start with data type optimization:

• Use strict types (e.g., numerics instead of strings).

• Avoid Nullable columns unless absolutely necessary.

• Choose minimal precision for numeric and date types[15][16].

For columns with fewer than 10,000 unique values, apply LowCardinality encoding to reduce storage and improve query performance[15][16].

Ordering keys are another critical element. Select columns that are frequently used in WHERE clauses and are highly correlated with other columns. Arrange these keys in ascending order of cardinality while balancing filtering efficiency[15].

Compression strategies can also make a big difference. Use ZSTD compression as a general-purpose option, Delta compression for sequences that increase monotonically, and T64 for sparse data or narrow ranges. Thanks to ClickHouse’s column-oriented architecture, data can often be compressed by up to 32:1, reducing network overhead significantly[16].

To minimize expensive JOIN operations, denormalize data by combining tables where possible. Use ClickHouse dictionaries for fast key-value lookups and leverage incremental materialized views to pre-compute aggregate values. This approach reduces the need for costly cross-network queries[15].

Batch inserts are crucial to minimizing merge overhead. For real-time data streams from edge devices, asynchronous inserts work best. Additionally, ClickHouse’s hash-based deduplication can handle connection timeouts and re-sent data efficiently[17].

With your schema optimized, the final piece of the puzzle is monitoring and maintenance.

Monitoring and Maintenance Tasks

Monitoring becomes even more critical in hybrid setups to maintain low-latency synchronization and overall system health. ClickHouse provides built-in metrics through tables like system.metrics, system.events, and system.asynchronous_metrics. These can be exported to tools like Prometheus or Graphite for centralized tracking[19].

Key areas to monitor include:

• CPU, memory, storage, and network usage on all nodes.

• Set max_threads to align with CPU cores.

• Configure memory limits (max_memory_usage, max_bytes_before_external_group_by, max_bytes_before_external_sort) to prevent resource overuse.

• Disable memory overcommit and avoid swap usage for better stability[14].

For server availability, monitor both individual nodes and overall cluster health. Use HTTP API endpoints like /ping and /replicas_status to check server status. Adjust the max_replica_delay_for_distributed_queries parameter to manage distributed queries effectively[19].

Security is a top priority in hybrid environments. Enforce TLS encryption for data in transit, review logs regularly for suspicious activity, and set query quotas to prevent resource abuse. Use named collections to securely manage credentials for external services. A real-world example of the importance of security came in March 2023, when Wiz Research discovered a publicly accessible ClickHouse database owned by DeepSeek AI. The issue was quickly resolved after notification, but it highlights the need for proper setup and vigilance[20].

To protect your data, use tools like clickhouse-backup to create backups and store them in S3. Test your high-availability procedures regularly[13].

When upgrading ClickHouse versions, do it one node per shard at a time. This approach ensures continuous operation while maintaining security and performance[14].

Finally, tools like Acceldata Pulse can simplify monitoring by automating visualizations and setting up alerts for anomalies or threshold breaches. As Gaurav Nagar, Co-Founder & Senior Architect, explains:

"Flawless real-time data observability accelerates data ROI. The need for comprehensive visibility with the ever-increasing sprawl of databases in the enterprise ecosystem has never been higher. It provides us with business agility, stability, and assurance of high-quality operations." - Gaurav Nagar [18]

With the right network setup, schema design, and monitoring in place, you can confidently manage ClickHouse® at scale in hybrid environments, ensuring performance and reliability.

Choosing the Right Solution for Your Hybrid Architecture

Selecting the ideal hybrid solution - Tinybird, self-managed ClickHouse®, or ClickHouse Cloud - requires careful consideration of performance, cost, and operational complexity. This decision plays a key role in optimizing hybrid edge-cloud performance and ensuring low-latency data synchronization.

Trade-Offs Between Solutions

Each option comes with its own strengths and trade-offs, which can significantly influence the success of your hybrid architecture. Understanding these differences is essential to align your choice with your organization's goals and capabilities.

One of the biggest factors to consider is operational overhead. Self-managed ClickHouse® offers complete control but demands significant expertise in areas like high availability, upgrades, and observability [10]. ClickHouse Cloud eases this burden by managing the infrastructure while still allowing granular control over database settings [7]. Tinybird goes a step further by abstracting the complexities entirely, providing tools like single-click API generation, managed HTTP streaming endpoints, and Git integration [7].

Performance is another critical consideration. Tinybird delivers performance comparable to ClickHouse on similar compute resources, with non-Enterprise plans achieving over 20 MB/s write throughput and handling 1,000+ queries per second with sub-50ms p95 query latency on shared infrastructure [7]. ClickHouse Cloud offers greater flexibility with compute sizing, enabling you to adjust resources to fit your hybrid workload [7]. Self-managed deployments provide the highest level of customization but require advanced expertise to fine-tune effectively.

When it comes to cost, the differences are notable. Hosting ClickHouse® yourself can lower infrastructure expenses but often increases development costs [7]. ClickHouse Cloud pricing ranges from $1 to $193 per month for development plans and $500 to over $100,000 for production environments [10]. Tinybird's usage-based pricing is particularly economical - 93% of non-enterprise customers on paid plans spend less than $100 per month, with the median cost for non-Enterprise workspaces falling below $10 [7].

These trade-offs lay the foundation for a practical decision-making framework.

Decision Framework and Recommendations

To choose the right solution, you need to weigh your organization's technical expertise, budget, and strategic goals. Here’s a guide to help you decide:

• Tinybird is the go-to option if you need a fast time-to-market and minimal operational overhead. It’s perfect for teams developing user-facing analytics features without dedicated database administrators. Tinybird’s seamless services for ingestion, API hosting, and observability make it especially appealing for startups or development teams focused on building products rather than managing infrastructure [7].

• ClickHouse Cloud is a solid choice for those seeking database control without the hassle of managing infrastructure. This solution suits teams with some ClickHouse experience who want a hosted environment that still allows database tuning. It’s particularly effective for hybrid workloads requiring specific optimizations but without the need for full operational responsibility [7].

• Self-managed ClickHouse® is ideal for organizations with advanced data engineering expertise and a need for maximum control. This option works well for companies with strict compliance requirements, on-premises infrastructure needs, or highly customized deployment demands. While it requires an investment in learning ClickHouse internals, it offers unparalleled flexibility and control over your database environment [7].

Your decision should also factor in your hybrid architecture’s unique demands. Edge-heavy setups with limited connectivity may benefit from self-managed solutions that can function independently. In contrast, cloud-centric deployments often thrive with managed services that handle variable workloads automatically.

The skill level of your team is another critical factor. Teams with deep database expertise can fully leverage the capabilities of self-managed ClickHouse®. Development teams with limited database administration experience might achieve better results with Tinybird’s simplified approach. Meanwhile, ClickHouse Cloud strikes a balance for teams with moderate ClickHouse knowledge who want some control but not the full operational responsibility.

Finally, consider your budget and growth expectations. Tinybird’s usage-based pricing adjusts naturally with your workload, making it a cost-effective choice for scaling. ClickHouse Cloud’s compute-based pricing is a good fit for predictable workloads with clear resource needs. Self-managed solutions offer the most cost control but require upfront investments in infrastructure and expertise.

The right solution depends on how well it aligns with your technical needs, operational capabilities, and business objectives. Making the right choice will ensure your hybrid system delivers low-latency synchronization and optimal performance across both edge and cloud components.

FAQs

How does Tinybird compare to ClickHouse® for low-latency data synchronization in hybrid architectures?

Tinybird and ClickHouse® both focus on delivering low-latency data solutions, but they serve distinct needs and user preferences.

Tinybird is a managed platform built on ClickHouse, tailored for real-time analytics. It simplifies the process by offering features like built-in data ingestion, API endpoints, and an optimized infrastructure. This makes it an excellent choice for developers seeking a scalable solution without the hassle of managing operations.

On the other hand, ClickHouse® is an open-source columnar database celebrated for its raw speed and adaptability. It's particularly suited for teams with the technical know-how to manage custom configurations and infrastructure, especially for large-scale, highly specific data projects. If ease of use and quick deployment are your priorities, Tinybird is the way to go. But if you need complete control and the ability to customize everything, ClickHouse® is the better fit.

What is Change Data Capture (CDC), and how does it improve data synchronization in hybrid edge-cloud systems?

Change Data Capture (CDC) is a method designed to streamline data synchronization in hybrid edge-cloud systems. Instead of handling entire datasets, it focuses on tracking and capturing only the changes made to the data. This approach cuts down on bandwidth usage and reduces processing demands, making it an efficient way to keep systems aligned.

With CDC, near real-time data replication becomes possible, which helps reduce latency and ensures that distributed systems stay consistent and up-to-date. By syncing only the modified records, CDC eliminates the risk of data silos and speeds up migration tasks. It plays a key role in maintaining smooth and dependable operations within hybrid system architectures.

What should I consider when deciding between self-managed ClickHouse®, ClickHouse Cloud, and Tinybird for hybrid edge-cloud architectures?

When weighing your options between self-managed ClickHouse®, ClickHouse Cloud, and Tinybird for hybrid edge-cloud setups, it's essential to think about control, scalability, and operational effort.

With a self-managed ClickHouse®, you get complete control over your infrastructure. However, this approach demands a high level of expertise to handle setup, maintenance, and scaling effectively. On the other hand, ClickHouse Cloud offers a fully managed, serverless solution that takes the hassle out of operations, making deployment and scaling much simpler. The trade-off? It’s less customizable and often comes with a higher price tag. Tinybird, which is built on ClickHouse, takes things a step further by abstracting infrastructure management entirely. It’s a great fit for developers aiming to create real-time analytics applications with minimal configuration and seamless scalability.

Ultimately, your decision will hinge on your team’s technical skills, how much control you require, and the time and resources you’re willing to allocate to managing infrastructure.