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Apache Spark vs Dremio: What are the differences?
Introduction
Apache Spark and Dremio are both popular tools used for data processing and analysis. While they share some similarities, there are key differences that set them apart from each other. Here are six important differences between Apache Spark and Dremio:
Architecture: Apache Spark follows a distributed computing architecture, allowing it to process large-scale datasets across a cluster of machines. On the other hand, Dremio follows a distributed SQL architecture that focuses on accelerating query performance using data lake engines.
Data Processing: Spark is a general-purpose data processing engine that supports various workloads, including batch processing, real-time streaming, and machine learning. Dremio, on the other hand, is specifically designed for SQL-based data processing tasks and offers high-speed query execution.
Data Sources: Spark is known for its versatility when it comes to data sources. It supports a wide range of data formats and can seamlessly integrate with various data storage systems, such as Hadoop Distributed File System (HDFS), Apache Cassandra, and Amazon S3. Dremio, on the other hand, focuses on providing optimization and self-service data access to data stored in data lakes, including popular file formats such as Parquet, JSON, and CSV.
SQL Optimization: While both Spark and Dremio support SQL queries, Dremio incorporates advanced query optimization techniques to improve query performance. It leverages query acceleration techniques like columnar in-memory caching, indexing, and reflection, which allows for faster query execution. Spark, on the other hand, doesn't provide built-in query acceleration and relies more on parallel processing capabilities.
Governance and Security: Dremio places a strong emphasis on data governance and security. It provides fine-grained access control, auditing, and data lineage features to ensure data compliance and security. Spark, on the other hand, does not have built-in governance and security features but can integrate with external tools to meet these requirements.
Data Catalog and Discovery: Dremio includes a built-in data catalog that provides a unified view of data from multiple sources within the data lake. It also offers data discovery capabilities, making it easier to explore and analyze data. In contrast, Spark does not provide a native data catalog and data discovery functionality, although it can be integrated with external tools like Apache Hive for similar capabilities.
In Summary, Apache Spark and Dremio differ in their architecture, data processing capabilities, support for different data sources, SQL optimization techniques, governance and security features, and data catalog and discovery functionalities.
We need to perform ETL from several databases into a data warehouse or data lake. We want to
- keep raw and transformed data available to users to draft their own queries efficiently
- give users the ability to give custom permissions and SSO
- move between open-source on-premises development and cloud-based production environments
We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.
You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.
But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.
Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.
You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.
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"
I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.
My question is which is the best tool to do the following:
- Create pipelines to ingest the data from multiple sources into the data lake
- Help me in aggregating and filtering data available in the data lake.
- Create new reports by combining different data elements from the data lake.
I need to use only open-source tools for this activity.
I appreciate your valuable inputs and suggestions. Thanks in Advance.
Hi Karunakaran. I obviously have an interest here, as I work for the company, but the problem you are describing is one that Zetaris can solve. Talend is a good ETL product, and Dremio is a good data virtualization product, but the problem you are describing best fits a tool that can combine the five styles of data integration (bulk/batch data movement, data replication/data synchronization, message-oriented movement of data, data virtualization, and stream data integration). I may be wrong, but Zetaris is, to the best of my knowledge, the only product in the world that can do this. Zetaris is not a dashboarding tool - you would need to combine us with Tableau or Qlik or PowerBI (or whatever) - but Zetaris can consolidate data from any source and any location (structured, unstructured, on-prem or in the cloud) in real time to allow clients a consolidated view of whatever they want whenever they want it. Please take a look at www.zetaris.com for more information. I don't want to do a "hard sell", here, so I'll say no more! Warmest regards, Rod Beecham.
Pros of Dremio
- Nice GUI to enable more people to work with Data3
- Connect NoSQL databases with RDBMS2
- Easier to Deploy2
- Free1
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
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Cons of Dremio
- Works only on Iceberg structured data1
Cons of Apache Spark
- Speed4
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