It processes batch and stream data using its own scalable engine. Upsolver is a fully-managed self-service data pipeline tool that is an alternative to Spark for ETL. But you still must perform a lot of optimization on the storage layer to improve query performance (for example, to compact small files on S3 ). Glue Studio is a serverless offering that also handles dependency resolution, job monitoring, and retries. Among the most prominent of these are:ĪWS Glue Studio is not a Spark alternative but rather a Spark “helper.” It is the component of AWS’ data integration service that provides a visual UI for creating, scheduling, running, and monitoring Spark-based ETL workflows on Amazon EMR, Amazon’s managed Spark service. There are managed service alternatives to Spark for ETL as well. See our post on open-source stream processing frameworks for more detail. It’s important to keep in mind that, while powerful, these open source frameworks are also complex and require extensive engineering effort to work properly. Apache Flume is designed to process large volumes of log data from web servers to systems based on the Hadoop Distributed File System (HDFS). Apache Flink is a framework and distributed processing engine for stateful computations over both unbounded and bounded data streams it treats batches as data streams with finite boundaries. Apache Storm is designed to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. These frameworks differ primarily in the type of data for which they’re intended. There are multiple ETL frameworks you can use in place of Spark. Depending on your data sources, you also may have to code your own connectors. Still, creating efficient ETL processes with Spark takes substantial manual effort to optimize Spark code, manage Spark clusters, and orchestrate workflows. Spark has often been the ETL tool of choice for wrangling datasets that typically are too large to transform using relational databases (big data) it can scale to process petabytes of data. And building ETL pipelines is a significant portion of a data engineer’s responsibilities. Spark use case: Extract-transform-load (ETL)ĮTL tasks are commonly required for any application that works with data. Read more about Spark Streaming and Spark Structured Streaming. Many organizations struggle to get to production with Spark Streaming as it has a high technical barrier to entry and requires extensive dedicated engineering resources. Also, it means that scheduling Spark jobs and managing them over a streaming data source requires extensive coding. But it may surprise you to know that Spark Streaming isn’t a pure streaming solution it breaks down data streams into micro-batches and so retains some of the challenges of batch processing, such as latency. Spark Streaming enables data engineers and data scientists to process real-time data from message queues such as Apache Kafka and AWS Kinesis, web APIs such as Twitter, and more. Further, people with the expertise required to use it efficiently and correctly in production systems are hard to find and expensive to hire.įor organizations that need fresher data than Spark’s batch processing can deliver, the Apache project released an extension of Apache Spark’s core API called Apache Spark Streaming. Using Spark requires knowledge of how distributed systems work. It executes batch, streaming, or machine learning workloads that require fast iterative access to large, complex datasets.Īrguably one of the most active Apache projects, Spark works best for ad-hoc jobs and large batch processes. What is Spark and what is it used for?Īpache Spark is a fast, flexible engine for large-scale data processing. In this article we look at 4 common use cases for Apache Spark, and suggest a few alternatives for each one. Many organizations wrestle with the complexity and engineering burden of using and managing Spark, or they might require fresher data than Spark’s batch processing is able to deliver. Spark is powerful and useful for diverse use cases, but it is not without drawbacks. It has become an essential tool for most developers and data scientists who work with big data. If you’re looking for a Spark alternative that offers true self-service with no data engineering bottlenecks, you can get our technical whitepaper to learn more about Upsolver.Īpache Spark is an open-source framework for distributed data processing. Trying to build a file system for your data lake? Check out our free ebook on data partitioning on S3.
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