Software Alternatives, Accelerators & Startups

LightStep VS Databricks

Compare LightStep VS Databricks and see what are their differences

LightStep logo LightStep

We deliver insights that put organizations back in control of their complex software apps.

Databricks logo Databricks

Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โ€ŽWhat is Apache Spark?
  • LightStep Landing page
    Landing page //
    2023-08-21
  • Databricks Landing page
    Landing page //
    2023-09-14

LightStep features and specs

  • Comprehensive Observability
    LightStep provides an extensive view of microservices performance, enabling developers to understand and troubleshoot complex architectures effectively.
  • Scalability
    Designed to handle large-scale applications, LightStep can efficiently manage data from millions of traces per second, making it suitable for enterprises with high demands.
  • Real-time Insights
    Offers real-time analysis of system performance, allowing teams to detect and resolve issues as they occur, minimizing downtime and service disruption.
  • Seamless Integration
    LightStep integrates well with popular development and operations tools, allowing teams to incorporate it into their existing workflows without much hassle.

Possible disadvantages of LightStep

  • Complex Setup
    Initial configuration and setup can be complex, potentially requiring specialized knowledge to optimize its capabilities effectively.
  • Cost
    Depending on the scale and usage, LightStep's pricing can be high, which might be a concern for startups and smaller companies with limited budgets.
  • Learning Curve
    Due to its comprehensive features, there might be a significant learning curve for new users to fully leverage all functions and insights it offers.
  • Data Privacy Concerns
    As with any observability tool, concerns around data privacy and compliance can arise, especially when dealing with sensitive or regulated data.

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.

LightStep videos

Lightstep Chronicles Review: The Shiniest Sci-Fi Visual Novel!

More videos:

  • Review - Lightstep Chronicles Review

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 LightStep and Databricks)
Monitoring Tools
100 100%
0% 0
Data Dashboard
0 0%
100% 100
Application Performance Monitoring
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 LightStep and Databricks

LightStep Reviews

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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

Databricks might be a bit more popular than LightStep. We know about 18 links to it since March 2021 and only 15 links to LightStep. 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.

LightStep mentions (15)

  • KubeCon + CloudNativeCon Europe 2023: Highlights from Amsterdam
    We focused on the observability ecosystem and took the time to interact with our friends from Lightstep, New Relic, Honeycomb, Dynatrace, Instana, and many more. With that in mind, keep an eye out for more integrations coming to Tracetest! - Source: dev.to / about 3 years ago
  • Top 9 Commercial Distributed Tracing Tools
    Lightstep bills itself as a platform for the reliability of cloud-native applications. The people behind Lightstep co-founded OpenTelemetry and OpenTracing, which gives them a unique perspective on the use cases of distributed tracing and the value of having a vendor-neutral tracing data format. - Source: dev.to / over 3 years ago
  • Observability - Types Of Vendor Pricing Models
    In the last 5 to 10 years, new Observability vendors have entered the market, including Honeycomb, Instana, Lightstep and Datadog. Similarly, traditional APM vendors such as Dynatrace, AppDynamics, and New Relic, as well as SIEM (and log management) vendors such as Splunk and Sumo Logic, have joined them in the Observability space too. Finally you also have major cloud providers such as AWS with their own... - Source: dev.to / over 3 years ago
  • KubeCon North America 2022: A Retrospective
    I spent Day 2 at the Colony Club to attend OTel Unplugged. This event was sponsored by Lightstep, Honeycomb, New Relic, Splunk, Dynatrace, Crowdstrike, and NGINX. I came into the event not knowing what to expect. I can sometimes clamp up when Iโ€™m around folks that I donโ€™t know, but because I was helping with the event check-in, I got to say hello to a number of the attendees, which helped break the ice. And it... - Source: dev.to / over 3 years ago
  • Grafana Phlare, open source database for continuous profiling at scale
    Https://lightstep.com, but thatโ€™s the only one :). - Source: Hacker News / over 3 years ago
View more

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: over 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 LightStep and Databricks, you can also consider the following products

NewRelic - New Relic is a Software Analytics company that makes sense of billions of metrics across millions of apps. We help the people who build modern software understand the stories their data is trying to tell them.

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

Honeycomb - Honeycomb is a powerful tool for complex/distributed systems, microservices, and databases.

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.

Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.

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.