Alexis Kestler
The Complete Technology Geek‘s Guide
Hey there! As a fellow data geek, I know how crucial it is to gain valuable real-time insights from the firehose of streaming data. The world runs on data now – whether it‘s detecting fraud, enabling smart trades or providing hyper-personalization. Streaming platforms are the mission-critical foundations that make all this possible.
So I‘ve put together this comprehensive guide on the best streaming data platforms for you (and nerds like us!) to quickly evaluate capabilities, use cases and technical architecture to determine the right fit. Let‘s get started!
Streaming Data 101 – A Quick Primer
Before we compare specific platforms, let me quickly go over what streaming data actually means and why you need it:
What is Data Streaming?
It refers to constant streams of live data from various sources like mobile devices, sensors, applications, databases and cloud services. This real-time data feed allows continuous capture and processing to drive rapid insights and decision making.
Key Use Cases and Benefits
Here are some common streaming data use cases across industries:
- Fraud detection – Analyze transactions, location, usage patterns etc. to identify threats.
- Algorithmic trading – Ingest market data feeds for high frequency trading analytics.
- Personalization – Segment users based on their real-time behavior data.
- Predictive analytics – Process weather sensor data to forecast storms or crop yields.
The key benefits provided by streaming pipelines include:
- Real-time analytics – Take actions based on latest data.
- Reduced data loss – Stream directly to storage.
- Flexibility – Adapt computed metrics as needs change.
- Scalability – Auto-scale your infrastructure.
Now let‘s explore platforms that allow you to realize these benefits!
Leading Data Streaming Platforms Compared
Click to expand full comparison
| Platform | Managed/Self-Hosted | Use Cases | Key Capabilities | Integrations | Learning Resources |
|---|---|---|---|---|---|
| Confluent Cloud | Managed | Real-time data pipelines, stream processing | – Serverless infrastructure – Infinite data retention – 99.9% uptime SLA |
160+ connectors, AWS/GCP services | Documentation, training courses |
| Aiven Kafka | Managed | Streaming data ingestion, stream processing | – Fully managed Kafka clusters – One-click scaling, updates – High availability |
MySQL, PostgreSQL, MongoDB | Getting started guides, status pages |
| Arroyo | Self-hosted | High performance streaming, real-time applications | – Cloud-native – Horizontally scalable – SQL stream processing |
Kafka, Redpanda, databases | Documentation, Discord community |
| Amazon Kinesis | Managed | Rapid data intake, serverless streaming | – Auto-scaling streams – Multi-zone data durability – Sub 10-min SLAs |
AWS Lambda, DynamoDB, S3 | Getting started guides, architecture docs |
| Databricks | Managed | Unified batch, streaming analytics | – Delta Live Tables – Auto-scaling jobs – Delta Lake support |
Azure, GCS storage, databases | Training courses, community forum |
| Qlik Data Streaming | Hybrid | Continuous data replication, CDC | – Broad source/target support – Cloud or self-hosted – Monitoring dashboard |
MySQL, Mongo, Postgres, SaaS APIs | Documentation, training videos |
| Fluvio | Self-hosted | High performance streaming applications | – Tight coupling to apps – Custom logic extensions – Secure streams |
PostgreSQL, object storage, gRPC/HTTP | Architecture docs, Discourse forum |
| Cloudera Stream Processing | Managed | Enterprise-scale stream analytics | – End-to-end governance – Multi-cloud deployment – High availability |
Schema registry, Kafka, Flink | Documentation, Cloudera Community |
| Striim Cloud | Managed | Streaming integration hub, data pipelines | – Over 100 turnkey connectors – Usage-based pricing – Dynamic SQL |
Google BigQuery, Snowflake, databases | Getting started guides, status pages |
| VK Streaming Platform | Managed | Massive scale data collection | – Advanced analytics – Smart data sourcing – Robust security |
Custom solutions | Not much documentation |
| HStream Platform | Self-hosted | Cost-efficient Kafka alternative | – SQL stream processing – Serverless option – Replayable streams |
Postgres, Redshift, S3 | Getting started guide |
Now let me share my views on some of the popular streaming platforms:
Confluent Cloud – The Enterprise Streaming Leader
Confluent Cloud is built on top of Apache Kafka specifically for highly scalable data streaming and processing. Its serverless offering and Kora storage engine deliver an order of magnitude better performance than managed Apache Kafka.
Over 70% of Fortune 100 companies now use Confluent to build their real-time data pipelines. Why?
For one – Confluent Cloud provides enterprise-grade confidence and reliability which is non-negotiable when dealing with business critical systems. Their architecture offers replication across availability zones coupled with 99.99% SLA driven uptime.
It also seamlessly integrates into existing cloud data and processing tools through 160+ out-of-the-box connectors. This interoperability allows leveraging other AWS, GCP and Azure analytics services.
And with their usage-based pricing model, costs scale linearly even for spiky workloads. Pricing starts at $99/month based on minimum data throughput needs.
In my opinion, Confluent Cloud is the go-to enterprise platform for mission-critical streaming pipelines. Their technology innovations and credibility with large customers set them apart despite higher costs.
Aiven Kafka – Fully Managed Apache Kafka Done Right
If you specifically need managed Apache Kafka without proprietary extensions, Aiven Kafka is a superb choice. They entirely abstract away the complexity of operating Kafka clusters at scale and provide easy deployment on all major clouds – AWS, GCP, Azure, DigitalOcean and more.
A key advantage of Aiven‘s offering is their expertise in running open-source data tools at scale with high reliability. They provide a 99.9% SLA along with auto-healing infrastructure that handles failures, deta corruption etc. seamlessly.
Combined with the convenience of one-click scaling, upgrades and repairs, this makes Aiven Kafka a breeze to operate at scale. Integrations are available for all common data stores like MySQL, MongoDB, S3 etc. to build robust pipelines.
Pricing is very reasonable too starting at $200/month depending on cloud provider costs. Overall, Aiven Kafka balances managed simplicity with Apache Kafka‘s versatility extremely well.
Databricks – Unified Analytics on Delta Lake
Trust Databricks to boil down complex big data concepts into easy to use abstractions! Their Delta Lakehouse platform powered by Apache Spark allows building pipelines for batch processing, real-time streaming and machine learning workflows.
Databricks uses Delta Live Tables (DLT) to hide this complexity behind simple table definitions. Just point your pipeline components to DLTs instead of manually orchestrating jobs. DLTs provide:
- Automated data quality checks using rule-based monitoring.
- Dynamic scaling of Spark workloads to cut costs.
- Native support for open Delta Lake format on cloud object stores.
On the streaming side specifically, Databricks runs blazing fast Spark Structured Streaming jobs under the hood. Performance tuning happens automatically.
Their 14-day free trial lets you test out these capabilities across AWS, GCP and Azure clouds. Overall, you get simplicity, versatility and scale in a tightly integrated platform.
Evaluating Your Streaming Data Platform Needs
With so many capable options, how do you pick the right one? Here is a step-by-step process:
1. Define Your Use Cases
First, identify your specific streaming needs – rapid data intake at high volumes, joining data feeds, in-stream processing to detect patterns, pushing data to data warehouses etc. This clarity helps match technical capabilities.
For example, algorithmic trading platforms need sub-second processing with custom logic while media analytics may involve high volume aggregation.
2. Estimate Throughput Volumes
Take into account current data volumes and expected growth over the next few years. This helps determine the baseline scale you‘ll need from the streaming architecture and infrastructure.
While all platforms here support auto-scaling, having buffer room is wise. You can refer to this throughput comparison chart I created:
| Platform | Max. Throughput |
|---|---|
| Confluent Cloud | 10s of millions events/sec |
| Aiven Kafka | 100,000 events/sec (~40TB daily) |
| Arroyo | 1+ million events/sec |
| Amazon Kinesis | Millions events/sec (multi-stream) |
| Databricks | 100s of millions events/sec |
3. Evaluate Reliability Requirements
Mission critical pipelines demand maximum resilience and uptime via multi-region deployments, replication etc. If data loss risks business outcomes, prioritize robust platforms.
Managed services like Confluent Cloud guarantee 99.99% uptime SLAs with auto-recovery while open-source options leave this to your implementation skills!
4. Review Integrations Needed
Whether you need to consume IoT telemetry feeds or syndicate data into a data lake, integration capabilities are key.
Options like Striim Cloud (100+ turnkey connectors) and Confluent Cloud (160+ connectors, Kafka integrations) stand out on this front.
5. Compare Architectures
While reviewing the technology stack powering the streaming platform, ask:
- Does it natively support in-stream processing for my use case?
- How durable and available is the underlying storage?
- Does it provide tools to monitor, secure and govern our streaming solution end-to-end?
6. Estimate Costs
Finally, model out projected costs depending on hosting, data volumes and processing needs. This gives clarity on the budget required.
For example, to handle 1 million events/day (~3KB each), my projected costs are:
- Confluent Cloud: $349/month
- Amazon Kinesis: $122/month
- Aiven Kafka: $200/month
I‘d pick Confluent Cloud here for their enterprise capabilities despite higher costs.
Key Takeaways
Here are the top lessons from this streaming platform guide:
- Clearly define your use cases first – data volumes, processing needs, integrations etc. – to pick the right platform. Go serverless if workloads are spiky.
- For mission critical systems, prioritize multi-zone resilience, proven architecture and 24/7 support services.
- Evaluate both fully managed platforms like Databricks and Confluent Cloud along with self-hosted open-source options like Fluvio and Arroyo.
- Throughput needs, connectors required and data governance capabilities are key aspects to compare.
- Model cost implications early on based on traffic forecasts. Watch out for hidden platform charges.
I hope you found this guide to be a detailed yet friendly resource for shortlisting streaming options! As you evaluate platforms, please feel free to drop any follow-up questions in the comments section below. I‘m happy to offer my inputs as a fellow data streaming enthusiast!
