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Apache Spark vs Trifacta: What are the differences?
Processing Paradigm: Apache Spark is designed for large-scale data processing using the concept of Resilient Distributed Datasets (RDDs) and in-memory computing, making it suitable for real-time processing and iterative algorithms. On the other hand, Trifacta focuses on data preparation tasks like cleaning, structuring, and enriching data to make it suitable for analysis, making it ideal for data wrangling and data cleaning processes.
User Interaction: Apache Spark provides APIs in various languages like Scala, Java, and Python for performing data processing tasks programmatically. In contrast, Trifacta offers a visual interface that allows users to interact with the data through a user-friendly GUI without the need for coding, making it more accessible to non-technical users.
Scalability: Apache Spark is known for its ability to scale up and handle large volumes of data efficiently across a distributed cluster of machines, enabling parallel processing. Trifacta, while capable of processing significant amounts of data, may not offer the same level of scalability as Apache Spark due to its focus on data preparation rather than computation on distributed clusters.
Data Sources: Apache Spark supports various data sources and file formats for input and output, including HDFS, Amazon S3, and Apache Hive, enabling seamless integration with different data storage systems. Trifacta, on the other hand, focuses on working with structured and semi-structured data from sources like databases, CSV files, and Excel spreadsheets, emphasizing data preparation for analytics rather than data ingestion from diverse sources.
Data Transformation Capabilities: Apache Spark provides a wide range of transformation and processing functions through its APIs, allowing users to manipulate data, run complex algorithms, and perform analytics tasks effectively. Trifacta specializes in providing diverse data transformation functionalities, such as data cleaning, normalization, and data structuring, with intuitive visual tools to enhance data quality and consistency before analysis.
Integration with Ecosystem: Apache Spark is part of the Hadoop ecosystem and integrates seamlessly with other Apache projects like Hadoop, Hive, and HBase, facilitating a unified big data processing environment. In comparison, Trifacta can integrate with various data storage platforms, BI tools, and data analytics solutions to streamline the data preparation workflow and enhance interoperability within the data ecosystem.
In Summary, Apache Spark excels in distributed data processing for real-time and iterative tasks, while Trifacta specializes in data preparation and wrangling through a visual interface, catering to different aspects of the data processing pipeline.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
Pros of Trifacta
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Cons of Apache Spark
- Speed4
Cons of Trifacta
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What is Apache Spark?
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