Activeloop Deep Lake vs Apache Flink
Overview
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Detailed Comparison
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. | 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. |
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance | 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 25.4K | GitHub Stars 8.9K |
GitHub Forks 13.7K | GitHub Forks 691 |
Stacks 534 | Stacks 1 |
Followers 879 | Followers 0 |
Votes 38 | Votes 0 |
Pros & Cons | |
Pros
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Integrations | |
What are some alternatives to Apache Flink, 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.
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.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
