Dremio vs s3-lambda

Overview

s3-lambda

Stacks4

Followers64

Votes0

GitHub Stars1.1K

Forks47

Dremio

Stacks116

Followers348

Votes8

Dremio vs s3-lambda: What are the differences?

Extracting Key Differences between Dremio and s3-lambda

Data Source Access: Dremio offers a self-service data platform for data engineers and analysts to directly access and query data from various sources, including relational databases, NoSQL databases, and cloud storage. On the other hand, s3-lambda specifically focuses on processing data stored in Amazon S3 buckets using AWS Lambda functions to transform and analyze data in a serverless architecture.
Query Processing Engine: Dremio utilizes a distributed query engine, which optimizes query performance by pushing down the processing closer to the data and enables interactive query analysis. In contrast, s3-lambda processes data through AWS Lambda functions, which are event-driven and operate on data stored in Amazon S3, providing a scalable and cost-effective way to handle data transformation tasks.
Data Transformation Capabilities: Dremio offers advanced data transformation capabilities, including SQL query optimization, data curation, and data lineage tracking, making it suitable for complex analytics and data engineering workflows. Conversely, s3-lambda focuses on event-driven data processing and transformation, providing a streamlined approach for processing data stored in Amazon S3 buckets using Lambda functions without the need for a separate processing layer.
Deployment and Management: Dremio requires deployment as a separate data platform that needs to be managed and maintained by organizations, offering a comprehensive solution for data analytics and processing. On the other hand, s3-lambda leverages AWS Lambda functions for data processing, making it easier to deploy and manage data transformation tasks in a serverless environment without the need for managing infrastructure.
Integration with Services: Dremio integrates with a wide range of data sources and BI tools, allowing seamless connectivity and data visualization capabilities for data analysis and reporting. In comparison, s3-lambda is tightly integrated with Amazon S3 and AWS Lambda, enabling data processing tasks to be executed within the AWS ecosystem and leveraging additional AWS services for scalability and performance enhancements.
Cost Considerations: Dremio may involve licensing costs based on the deployment model and usage, while offering a unified platform for data access and analytics. On the other hand, s3-lambda incurs costs based on the usage of AWS Lambda functions and other associated services, providing a pay-as-you-go pricing model for data processing tasks within the AWS environment.

In Summary, Dremio provides a comprehensive self-service data platform with advanced analytics capabilities, while s3-lambda focuses on serverless data processing and transformation specifically for data stored in Amazon S3 buckets.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on s3-lambda, Dremio

karunakaran

Consultant

Jun 26, 2020

Needs advice

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.

80.4k views80.4k

Comments

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

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.

319k views319k

Comments

Detailed Comparison

s3-lambda	Dremio
s3-lambda enables you to run lambda functions over a context of S3 objects. It has a stateless architecture with concurrency control, allowing you to process a large number of files very quickly. This is useful for quickly prototyping complex data jobs without an infrastructure like Hadoop or Spark.	Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.
-	Democratize all your data; Make your data engineers more productive; Accelerate your favorite tools; Self service, for everybody
Statistics
GitHub Stars 1.1K	GitHub Stars -
GitHub Forks 47	GitHub Forks -
Stacks 4	Stacks 116
Followers 64	Followers 348
Votes 0	Votes 8
Pros & Cons
No community feedback yet	Pros 3 Nice GUI to enable more people to work with Data 2 Easier to Deploy 2 Connect NoSQL databases with RDBMS 1 Free Cons 1 Works only on Iceberg structured data
Integrations
Amazon S3 AWS Lambda	Amazon S3 Python Tableau Azure Database for PostgreSQL Qlik Sense PowerBI

What are some alternatives to s3-lambda, Dremio?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Related Comparisons

Dremio vs s3-lambda: What are the differences?

Extracting Key Differences between Dremio and s3-lambda

Data Source Access: Dremio offers a self-service data platform for data engineers and analysts to directly access and query data from various sources, including relational databases, NoSQL databases, and cloud storage. On the other hand, s3-lambda specifically focuses on processing data stored in Amazon S3 buckets using AWS Lambda functions to transform and analyze data in a serverless architecture.
Query Processing Engine: Dremio utilizes a distributed query engine, which optimizes query performance by pushing down the processing closer to the data and enables interactive query analysis. In contrast, s3-lambda processes data through AWS Lambda functions, which are event-driven and operate on data stored in Amazon S3, providing a scalable and cost-effective way to handle data transformation tasks.
Data Transformation Capabilities: Dremio offers advanced data transformation capabilities, including SQL query optimization, data curation, and data lineage tracking, making it suitable for complex analytics and data engineering workflows. Conversely, s3-lambda focuses on event-driven data processing and transformation, providing a streamlined approach for processing data stored in Amazon S3 buckets using Lambda functions without the need for a separate processing layer.
Deployment and Management: Dremio requires deployment as a separate data platform that needs to be managed and maintained by organizations, offering a comprehensive solution for data analytics and processing. On the other hand, s3-lambda leverages AWS Lambda functions for data processing, making it easier to deploy and manage data transformation tasks in a serverless environment without the need for managing infrastructure.
Integration with Services: Dremio integrates with a wide range of data sources and BI tools, allowing seamless connectivity and data visualization capabilities for data analysis and reporting. In comparison, s3-lambda is tightly integrated with Amazon S3 and AWS Lambda, enabling data processing tasks to be executed within the AWS ecosystem and leveraging additional AWS services for scalability and performance enhancements.
Cost Considerations: Dremio may involve licensing costs based on the deployment model and usage, while offering a unified platform for data access and analytics. On the other hand, s3-lambda incurs costs based on the usage of AWS Lambda functions and other associated services, providing a pay-as-you-go pricing model for data processing tasks within the AWS environment.

Dremio vs s3-lambda

Overview

Dremio vs s3-lambda: What are the differences?