Activeloop Deep Lake vs AWS Glue
Overview
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.
CLI (Node.js)
Manual
Detailed Comparison
A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. | It retains all the features you love from data lakes, with one key twist. Deep Lake is explicitly built for deep learning workflows with image, audio, and video datasets. This saves time on building complex data infrastructure, & enables shipping AI models into production much faster. |
Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.; Integrated - AWS Glue is integrated across a wide range of AWS services.; Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.; Developer Friendly - AWS Glue generates ETL code that is customizable, reusable, and portable, using familiar technology - Scala, Python, and Apache Spark. You can also import custom readers, writers and transformations into your Glue ETL code. Since the code AWS Glue generates is based on open frameworks, there is no lock-in. You can use it anywhere. | Storage agnostic API;
Compressed storage;
Lazy Numpy-like indexing;
Dataset version control;
Integrations with deep learning frameworks;
Distributed transformations;
100+ most-popular image, video, and audio datasets available in seconds;
Instant visualization support in Activeloop platform |
Statistics | |
GitHub Stars - | GitHub Stars 8.9K |
GitHub Forks - | GitHub Forks 691 |
Stacks 461 | Stacks 1 |
Followers 819 | Followers 0 |
Votes 9 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
What are some alternatives to AWS Glue, Activeloop Deep Lake?
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.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Presto
Distributed SQL Query Engine for Big Data
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.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
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.
Apache Kylin
Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.
