Stale data short-circuits timely decision-making. Spark Streaming and its next-generation counterpart, Spark Structured Streaming, are designed for processing live data streams. They enable a variety of streaming data use cases where actions need to be taken on data in near real-time. However, Spark Streaming and Spark Structured Streaming both have drawbacks that may make deploying them less than ideal for certain use cases. In this article, we’ll examine these two stream processing engines, looking at how they work, where they’re used and the tradeoffs required for applications that rely on stream data. We’ll also introduce Dynamic Tables, as well as Snowpipe Streaming, Snowflake’s latest ingestion offering, highlighting its role in enabling low-latency streaming data pipelines that feed dashboards, machine learning models and applications.
What is Spark Streaming?
Apache Spark is an open-source, data processing framework designed for use with real-time data applications. It is relatively easy to scale and is useful for large-scale data processing, making it a popular framework for AI, ML and other big data applications. Spark can integrate with a variety of data sources and supports functional, declarative, and imperative programming styles.
Spark Streaming was an extension of the core Apache Spark API. It’s what enabled Spark to receive real-time streaming data from sources like Kafta, Flume and the Hadoop Distributed File System. It also allowed Spark to push out data to live dashboards, file systems and databases, providing near real-time data ingestion. Spark Streaming has been replaced with a new, next-generation streaming engine called Spark Structured Streaming. As a legacy project, Spark Streaming is no longer being updated.
What is Spark Structured Streaming?
Spark Structured Streaming is the updated version of Spark Streaming included as part of the Spark 2.0 release. Like Spark Streaming, Spark Structured Streaming is the Spark API for stream processing, enabling developers to take batch mode operations conducted via Spark’s APIs and run them for streaming applications. Although both old and new engines share the same underlying data polling architecture, Spark Structured Streaming leverages Dataframe of Dataset APIs, a change that optimizes processing and provides additional options for aggregations and other types of operations. Unlike its predecessor, Spark Structured Streaming is built on the Spark SQL library, eliminating some of the challenges with fault and strangler handling encountered with Spark Streaming.
Limitations of Spark Streaming and Spark Structured Streaming
Although Spark Structured Streaming represents an improvement, it may not be the best choice for certain streaming data analytics use cases. Here are some things to consider.
Expense
Spark is an in-memory processing system, making it heavily reliant on RAM to store and manipulate data. When it comes to low latency streaming data and scaling, the expense grows significantly. This reliance on in-memory computations for streaming data analytics use cases makes it an even more expensive choice.
Complex setup and management
Spark Structured Streaming is a useful tool for streaming data analytics applications, but entry into the Spark ecosystem requires deep familiarity with Apache Spark concepts. In addition, setting up ETL pipelines for Spark requires users to write programming code to optimize the data ingestion process.
Too many options to choose from
Originally developed solely as an on-premises solution, Apache Spark can be a challenge to scale for use in cloud solutions. Optimizing Spark for the cloud requires time-consuming management tasks and the use of an orchestration layer. Ensuring streaming data analytics applications are running smoothly requires successfully navigating checkpoint complexity, partitioning, shuffling and spilling to local disks.
Steep learning curve
With so much prior knowledge required and an extensive setup, using Spark Structured Streaming can represent a significant investment in time and expertise. Streaming data platforms such as Snowflake offer a significantly lower barrier to the world of streaming data analytics.
Snowpipe Streaming and Dynamic Tables: Advanced features for streaming data
Snowflake supports the ingestion of streaming data, allowing organizations to accelerate their streaming data analytics programs. Snowpipe Streaming offers a suite of advanced features for building more efficient and effective real-time data applications, including modern traffic management solutions. With Snowflake Streaming, businesses across industries can build and maintain the modern, resilient streaming data ingestion and analytics infrastructure required to leverage real-time traffic data.
Simple options with managed infrastructure
Snowflake is a fully managed service, running entirely on cloud infrastructure. This frees businesses to focus their full attention on extracting value from their stream data, rather than on maintaining the infrastructure required to ingest and analyze it. Snowflake Data Cloud requires no hardware (virtual or physical) to select, install, configure or manage, and very little software to install, configure or manage. All ongoing maintenance, management, upgrades and tuning are handled by Snowflake.
Break the streaming and batch barrier
Snowflake users can ingest real-time and historical data directly into Snowflake. With Snowpipe Streaming, data engineers and developers no longer need to stitch together a patchwork of different systems and tools to work with real-time streaming and batch data in one single system. Snowpipe Streaming resolves infrastructure management complexity, serving as a native streaming data ingestion offering to the Snowflake Data Cloud.
Serverless row-set ingest data directly into Snowflake
Leverage serverless row-set streaming ingestion with Snowpipe Streaming, seamlessly ingesting your stream data directly into Snowflake. This stream data ingestion method simplifies the creation of streaming data pipelines with less than five-second median latencies, ordered insertions of rows and serverless scalability to support throughputs of gigabytes per second. With Snowpipe Streaming, organizations have the stream data infrastructure required to handle all volumes of data at low cost and low latency without manual work. Resource scaling, query ordering and availability are automatically handled by Snowflake, freeing in-house teams to focus on other activities, such as developing and engineering new streaming data use cases.
Incrementally transform your data where it lives with Dynamic Tables
Dynamic Tables, now in public preview, supports more complex data transformations and can dramatically simplify streaming data pipelines by allowing organizations to incrementally transform their data where it lives in Snowflake. Dynamic Tables form building blocks of declarative data transformation pipelines, simplifying data engineering in Snowflake and providing a reliable, cost-effective and automated way to transform your data for consumption. Rather than defining data transformation steps as a series of tasks and then monitoring dependencies and scheduling, you can simply define the end state of the transformation using dynamic tables and leave the complex pipeline management to Snowflake.
Elastic storage and compute resources
Snowflake automatically grows or shrinks capacity based on current Snowpipe load. With access to Snowflake’s unique multi-cluster shared data architecture, organizations can efficiently tap into the performance, scale, elasticity and concurrency required for powering even the most complex, resource-intensive real-time data use cases.
Complement Snowpipe Streaming with the Snowpipe API/pairing Snowpipe Streaming with Snowflake Connector for Kafka
The Snowpipe Streaming API opens up new opportunities for working with stream data in Snowpipe. Calling the Snowpipe Streaming API prompts low-latency loads of streaming data rows using the Snowflake Ingest SDK and your own managed application code. The streaming ingest API writes rows of data directly to Snowflake tables, unlike bulk data loads that write data from staged files. This architecture results in lower load latencies and lower costs for loading similar volumes of data, making it ideally suited for handling real-time data streams. Snowpipe Streaming can also be paired with the Snowflake Connector for Kafka that reads data from one or more Apache Kafka topics and loads stream data directly into Snowflake tables.
The future of stream data with Snowflake
Spark Streaming and Spark Structured Streaming are used in a range of streaming data use cases. But as the complexity of working with stream data increases, streaming data platforms such as Snowflake provide an easier-to-use and cost-effective alternative. With Snowpipe Streaming, organizations can seamlessly load continuous data as it arrives, whenever it arrives. Snowflake’s suite of advanced features and fully managed streaming data infrastructure allows organizations to capture the full value of their streaming data.