The recent acquisition of Rockset and its impending service sunset in September has prompted a search for alternative solutions. Rockset has been pivotal for many businesses, particularly in facilitating real-time analytics by integrating seamlessly with NoSQL databases like DynamoDB. As we approach the discontinuation of Rockset’s services, it’s crucial to explore viable replacements and understand the necessary adjustments in your data stack to ensure results are delivered in just a few seconds.

Understanding Rockset and Its Alternatives

Rockset has been a cornerstone for many businesses, providing a cloud-based real-time analytics database known for its high performance and scalability. As the service approaches its sunset, it’s imperative for businesses to explore alternative solutions that can meet their real-time analytics needs. Understanding the strengths and weaknesses of Rockset and its alternatives is crucial for making an informed decision.

Rockset’s ability to handle real-time data ingestion and complex queries has made it a popular choice for integrating with NoSQL databases like DynamoDB. However, with its impending discontinuation, businesses must identify alternative solutions that offer similar capabilities. The goal is to find a real-time analytics database that can seamlessly integrate with existing data sources, ensuring consistent performance and the ability to gain insights from data streams in a matter of seconds.

The Two Essential Rockset Components to Replace

Rockset delivered a comprehensive solution consisting of two primary components: real-time data ingestion and an analytical (OLAP) database. These features enabled the rapid processing and querying of data. Data science plays a crucial role in modern analytics solutions, integrating artificial intelligence and machine learning to provide real-time insights and data-driven decision-making. In its absence, businesses might need to deploy two distinct services to fill the gap, although some providers offer bundled solutions with multiple integrated services. Machine learning can further enhance data management and analytics by automating processes and optimizing data pipelines in real-time.

Component 1: Data Ingestion and Integration

Historically, Rockset connected directly to data sources like DynamoDB, replicating data in near-real-time. Data science can automate data pipelines and improve real-time data ingestion, making it easier for businesses to gain timely insights. With its departure, alternatives for handling data ingestion include:

• Traditional ETL Tools: Generally suited for batch uploads rather than real-time streaming. Examples of common ETL tools include FiveTran and Airbyte
• Streaming ETL Tools: These are designed for real-time data flow, accommodating the immediate data streaming needs that batch ETL tools cannot meet. There are a few new players in this space including Artie and Streamkap who are working on integrations for Dynamo.
• Destination Specific Ingestion and Integration Products: Tools within the AWS ecosystem, like S3 and Redshift, facilitate data syncing from DynamoDB.
• In-House Data Pipelines: Building custom pipelines is an option, albeit with considerable resource investment in development and maintenance.

Component 2: OLAP Database

Rockset excelled as an analytics database, optimized for high-speed, complex queries. Machine learning can further optimize data queries and enhance analytics capabilities by automating processes and improving data management. To replicate this, consider these alternatives:

• Analytics Databases: Products like ClickHouse and SingleStore offer similar capabilities for fast, efficient query handling.
• Traditional Data Warehouses: While solutions like AWS Redshift are designed for large datasets, they may not always cater to the specific, quick-response analytics typical of Rockset.
• Specialized Solutions: Technologies like Pinot and StarTree focus on specific aspects of analytics and data management.

Alternative Solutions to Rockset

Adapting to Rockset’s absence involves selecting an appropriate database and corresponding data ingestion method. Alternative solutions must deliver results in just a few seconds to meet the demands of user-facing analytics. Here’s a breakdown of potential alternatives:

ClickHouse Ecosystem

Clickhouse provides a robust analytical database framework, suitable for large datasets and rapid queries. Data science can help manage these large datasets and optimize real-time queries in the ClickHouse ecosystem. To integrate data from DynamoDB:

• Dynamo → (Kinesis / ClickPipe) → ClickHouse: This pathway leverages AWS Kinesis and ClickPipe, ensuring data streams through with minimal delay (approximately 5+ seconds).
• Dynamo → S3 → ClickHouse: Exporting data from Dynamo to S3 is also an option as ClickHouse can ingest data directly from S3 while AWS has built-in solutions for exporting to S3. This method does require batch exports though so will not be streaming or realtime unlike the solution above.

SingleStore

For integrating with SingleStore, consider:

• Dynamo → (Traditional ETL) → SingleStore: Utilize ETL tools like Fivetran or Airbyte for data transfer, though this method involves batch processing rather than real-time updates.

Machine learning can significantly enhance data management and analytics in SingleStore by automating processes and improving the effectiveness of real-time optimization.

Redshift

AWS’s Redshift offers a structured, albeit less immediate, alternative for handling large datasets from DynamoDB. Data science can optimize data pipelines and enhance real-time analytics in Redshift, providing businesses with valuable insights and data-driven decision-making capabilities.

• AWS Dynamo to Redshift Connector : This beta feature simplifies the integration process, though it’s tailored more towards batch processing.

Evaluating Alternatives

When evaluating alternatives to Rockset, several key factors come into play. These include performance and cost-effectiveness, real-time loading and integrations, and the ability to handle complex queries and large datasets. Each of these factors is critical in ensuring that the chosen solution can meet the demands of real-time analytics.

Performance and Cost-Effectiveness

A robust alternative to Rockset should deliver fast performance while being cost-effective. This means it should handle high volumes of data and complex queries efficiently without incurring prohibitive costs. Solutions like ClickHouse and Apache Druid are known for their high performance and scalability, often at a lower cost than Rockset. These platforms are designed to manage large datasets and execute complex queries quickly, making them suitable for businesses looking to maintain fast performance without breaking the bank.

Real-Time Loading and Integrations

Real-time loading and integrations are essential for any real-time analytics database. A suitable alternative to Rockset should be capable of handling real-time data streams and integrating with a variety of data sources, including NoSQL databases and data warehouses. Solutions like Apache Pinot and Materialize excel in this area, offering real-time loading and seamless integrations with popular data sources. These platforms ensure that data is ingested and processed in real time, allowing businesses to perform real-time analytics and gain insights from their data streams without delay.

Best Practices for Transitioning from Rockset

Transitioning from Rockset to an alternative solution requires careful planning and execution. Here are some best practices to consider:

Change Data Capture and Database Joins

Change data capture (CDC) is a critical component of real-time analytics, enabling the continuous capture of changes in data. A good alternative to Rockset should efficiently handle CDC and database joins. Solutions like Apache Druid and others offer CDC and database joins, each with different approaches and trade-offs.

When transitioning from Rockset, it’s essential to evaluate the CDC and database join capabilities of the alternative solution. This includes understanding the performance and cost implications of CDC and database joins, as well as their impact on data consistency and accuracy. By thoroughly assessing these features, businesses can ensure a smooth transition and maintain the integrity of their real-time analytics.

By following these best practices and evaluating the key factors mentioned above, businesses can ensure a smooth transition from Rockset to an alternative solution that meets their real-time analytics needs. This approach will help maintain consistent performance and enable businesses to continue gaining valuable insights from their data streams.

Next Steps

As Rockset phases out, a variety of alternative solutions emerge, each capable of integrating with DynamoDB data for analytics purposes. Machine learning can ensure robust and effective analytics capabilities during this transition. The choice of technology will depend on specific business needs, particularly regarding real-time processing and query complexity.

For further assistance or to discuss these alternatives in detail, feel free to reach out to the Explo team. We’re here to help you navigate this transition smoothly and ensure your analytics capabilities remain robust and effective.

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