Software Alternatives, Accelerators & Startups

Hevo Data VS Databricks

Compare Hevo Data VS Databricks and see what are their differences

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Hevo Data logo Hevo Data

Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. Get near real-time data pipelines for reporting and analytics up and running in just a few minutes. Try Hevo for Free today!

Databricks logo Databricks

Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โ€ŽWhat is Apache Spark?
  • Hevo Data Landing page
    Landing page //
    2023-02-18

Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. It helps data teams streamline and automate org-wide data flows that result in a saving of ~10 hours of engineering time/week and 10x faster reporting, analytics, and decision making.

The platform supports 100+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services. Over 500 data-driven companies spread across 35+ countries trust Hevo for their data integration needs.

Try Hevo today and get your fully managed data pipelines up and running in just a few minutes.

  • Databricks Landing page
    Landing page //
    2023-09-14

Hevo Data features and specs

  • Data Extraction and Loading
    Integrate and manage data from 100+ sources
  • Data Transformation
    Run pre-load data transformation
  • Customer Support
    24/7 Live chat support

Databricks features and specs

  • Unified Data Analytics Platform
    Databricks integrates various data processing and analytics tools, offering a unified environment for data engineering, machine learning, and business analytics. This integration can streamline workflows and reduce the complexity of data management.
  • Scalability
    Databricks leverages Apache Spark and other scalable technologies to handle large datasets and high computational workloads efficiently. This makes it suitable for enterprises with significant data processing needs.
  • Collaborative Environment
    The platform offers collaborative notebooks that allow data scientists, engineers, and analysts to work together in real-time. This enhances productivity and fosters better communication within teams.
  • Performance Optimization
    Databricks includes various performance optimization features such as caching, indexing, and query optimization, which can significantly speed up data processing tasks.
  • Support for Various Data Formats
    The platform supports a wide range of data formats and sources, including structured, semi-structured, and unstructured data, making it versatile and adaptable to different use cases.
  • Integration with Cloud Providers
    Databricks is designed to work seamlessly with major cloud providers like AWS, Azure, and Google Cloud, allowing users to easily integrate it into their existing cloud infrastructure.

Possible disadvantages of Databricks

  • Cost
    Databricks can be expensive, especially for large-scale deployments or high-frequency usage. It may not be the most cost-effective solution for smaller organizations or projects with limited budgets.
  • Complexity
    While powerful, Databricks can be complex to set up and manage, requiring specialized knowledge in Apache Spark and cloud infrastructure. This might lead to a steeper learning curve for new users.
  • Dependency on Cloud Providers
    Being heavily integrated with cloud providers, Databricks might face issues like vendor lock-in, where switching providers becomes difficult or costly.
  • Limited Offline Capabilities
    Databricks is primarily designed for cloud environments, which means offline or on-premise capabilities are limited, posing challenges for organizations with strict data governance policies.
  • Resource Management
    Efficiently managing and allocating resources can be challenging in Databricks, especially in large multi-user environments. Mismanagement of resources could lead to increased costs and reduced performance.

Analysis of Hevo Data

Overall verdict

  • Hevo Data is generally considered a good choice for businesses that require efficient and reliable data integration solutions. Its features and performance make it a viable option for organizations looking to enhance their data workflows.

Why this product is good

  • Hevo Data is often praised for its user-friendly interface, easy setup process, and reliable performance in data integration. It offers automated data pipelines that help reduce manual effort and improve data accuracy, making it a popular choice among businesses looking to streamline their data operations. Additionally, it supports numerous data sources and destinations, offering flexibility and scalability to accommodate growing data needs.

Recommended for

    Hevo Data is recommended for businesses of all sizes that are seeking an easy-to-use platform for automating their data integration processes. It is particularly beneficial for teams that may not have extensive technical expertise but still need to manage complex data environments effectively. Companies looking for a scalable solution to handle real-time data streaming and transformation will also find Hevo Data beneficial.

Hevo Data videos

Getting Started with Hevo - An Overview

More videos:

  • Tutorial - Load Data from AWS S3 to Data Warehouse
  • Tutorial - ETL REST API Data to a Data Warehouse
  • Demo - Data Transformations on Hevo

Databricks videos

Introduction to Databricks

More videos:

  • Tutorial - Azure Databricks Tutorial | Data transformations at scale
  • Review - Databricks - Data Movement and Query

Category Popularity

0-100% (relative to Hevo Data and Databricks)
Data Integration
100 100%
0% 0
Data Dashboard
0 0%
100% 100
ETL
100 100%
0% 0
Big Data Analytics
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Hevo Data and Databricks

Hevo Data Reviews

Best ETL Tools: A Curated List
Hevo Data is a cloud-based ETL/ELT service that allows users to build data pipelines easily. Launched in 2017, Hevo provides a low-code platform, giving users more control over mapping sources to targets and performing simple transformations using Python scripts or a drag-and-drop editor (currently in Beta). While Hevo is ideal for beginners, it has some limitations compared...
Source: estuary.dev
Top 11 Fivetran Alternatives for 2024
Hevo Data is a no-code SaaS data pipeline platform that started as a cloud service in 2017. Hevo is primarily ELT but has been adding some row-based ETL support.
Source: estuary.dev
15+ Best Cloud ETL Tools
Hevo Data is one of the leading open-source ETL tools. It is a cloud-based, no-code data pipeline solution with ETL functionality for efficient data integration and management across all your systems. It provides easy data collection and reporting capabilities that can help your business ensure that accurate and real-time data is always available.
Source: estuary.dev
Top 14 ETL Tools for 2023
Hevo Data is an ETL data integration platform with over 100 pre-built connectors to databases, cloud storage, and SaaS sources. Users can define their own pre-load transformations in Hevo Data using Python. Hevo Data supports the most popular data warehouse destinations, including Redshift, BigQuery, and Snowflake.
Top 10 Fivetran Alternatives - Listing the best ETL tools
โ€Hevo Data has ETL, ELT, and reverse-ETL capabilities, and is code-free with integrations to various tools and data warehouses. For non-technical users who want to get up and running with their data, Hevo can help.
Source: weld.app

Databricks Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Databricks notebooks are a popular tool for developing code and presenting findings in data science and machine learning. Databricks Notebooks support real-time multilingual coauthoring, automatic versioning, and built-in data visualizations.
Source: lakefs.io
7 best Colab alternatives in 2023
Databricks is a platform built around Apache Spark, an open-source, distributed computing system. The Databricks Community Edition offers a collaborative workspace where users can create Jupyter notebooks. Although it doesn't offer free GPU resources, it's an excellent tool for distributed data processing and big data analytics.
Source: deepnote.com
Top 5 Cloud Data Warehouses in 2023
Jan 11, 2023 The 5 best cloud data warehouse solutions in 2023Google BigQuerySource: https://cloud.google.com/bigqueryBest for:Top features:Pros:Cons:Pricing:SnowflakeBest for:Top features:Pros:Cons:Pricing:Amazon RedshiftSource: https://aws.amazon.com/redshift/Best for:Top features:Pros:Cons:Pricing:FireboltSource: https://www.firebolt.io/Best for:Top...
Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
Databricks is a simple, fast, and collaborative analytics platform based on Apache Spark with ETL capabilities. It accelerates innovation by bringing together data science and data science businesses. It is a fully managed open-source version of Apache Spark analytics with optimized connectors to storage platforms for the fastest data access.
Source: visual-flow.com
Top Big Data Tools For 2021
Now Azure Databricks achieves 50 times better performance thanks to a highly optimized version of Spark. Databricks also enables real-time co-authoring and automates versioning. Besides, it features runtimes optimized for machine learning that include many popular libraries, such as PyTorch, TensorFlow, Keras, etc.

Social recommendations and mentions

Based on our record, Databricks should be more popular than Hevo Data. It has been mentiond 18 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Hevo Data mentions (10)

  • Top ETL Tools for MongoDB in 2025: Which One Fits Your Use Case?
    Hevo Data positions itself as a no-code ETL platform with native MongoDB destination support and over 150 pre-built connectors. The platform emphasizes ease of use while providing real-time data replication and transformation capabilities that don't require technical expertise to implement. - Source: dev.to / 11 months ago
  • Understanding the MLOps Lifecycle
    Some popular tools for data extraction are Airbyte, Fivetran, Hevo Data, and many more. - Source: dev.to / over 1 year ago
  • Quick tip: Replicating a MongoDB Atlas database to SingleStoreDB Cloud using Hevo Data
    In a previous article, we used open-source Airbyte to create an ELT pipeline between SingleStoreDB and Apache Pulsar. We have also seen in another article several methods to ingest MongoDB JSON data into SingleStoreDB. In this article, weโ€™ll evaluate a commercial ELT tool called Hevo Data to create a pipeline between MongoDB Atlas and SingleStoreDB Cloud. Switching to SingleStoreDB has many benefits, as described... - Source: dev.to / over 3 years ago
  • Best methods for pulling data from IBM DB2 (AS/400) to Snowflake?
    One of my customers just purchased Precisely to extract from their iSeries machines into Snowflake. Hevo can also do it. Source: over 3 years ago
  • Lowest latency dynamodb to redshift sync?
    I've been looking at Hevo data as well, and they certainly make the setup/maintenance a lot easier, but they have a latency of 5-10 minutes. What's the minimum lowest latency that can be achieved with aws for syncing dynamodb to redshift? Source: almost 4 years ago
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Databricks mentions (18)

  • Platform Engineering Abstraction: How to Scale IaC for Enterprise
    Vendors like Confluent, Snowflake, Databricks, and dbt are improving the developer experience with more automation and integrations, but they often operate independently. This fragmentation makes standardizing multi-directional integrations across identity and access management, data governance, security, and cost control even more challenging. Developing a standardized, secure, and scalable solution for... - Source: dev.to / almost 2 years ago
  • dolly-v2-12b
    Dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAIโ€™s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA). Source: about 3 years ago
  • Clickstream data analysis with Databricks and Redpanda
    Global organizations need a way to process the massive amounts of data they produce for real-time decision making. They often utilize event-streaming tools like Redpanda with stream-processing tools like Databricks for this purpose. - Source: dev.to / almost 4 years ago
  • DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
    Databricks, a data lakehouse company founded by the creators of Apache Spark, published a blog post claiming that it set a new data warehousing performance record in 100 TB TPC-DS benchmark. It was also mentioned that Databricks was 2.7x faster and 12x better in terms of price performance compared to Snowflake. - Source: dev.to / about 4 years ago
  • A Quick Start to Databricks on AWS
    Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward. - Source: dev.to / about 4 years ago
View more

What are some alternatives?

When comparing Hevo Data and Databricks, you can also consider the following products

Fivetran - Fivetran offers companies a data connector for extracting data from many different cloud and database sources.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Stitch - Consolidate your customer and product data in minutes

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

Airbyte - Replicate data in minutes with prebuilt & custom connectors

Looker - Looker makes it easy for analysts to create and curate custom data experiencesโ€”so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.