Software Alternatives, Accelerators & Startups

LightStep VS Google BigQuery

Compare LightStep VS Google BigQuery and see what are their differences

LightStep logo LightStep

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

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.
  • LightStep Landing page
    Landing page //
    2023-08-21
  • Google BigQuery Landing page
    Landing page //
    2023-10-03

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.

Google BigQuery features and specs

  • Scalability
    BigQuery can effortlessly scale to handle large volumes of data due to its serverless architecture, thereby reducing the operational overhead of managing infrastructure.
  • Speed
    It leverages Google's infrastructure to provide high-speed data processing, making it possible to run complex queries on massive datasets in a matter of seconds.
  • Integrations
    BigQuery easily integrates with various Google Cloud Platform services, as well as other popular data tools like Looker, Tableau, and Power BI.
  • Automatic Optimization
    Features like automatic data partitioning and clustering help to optimize query performance without requiring manual tuning.
  • Security
    BigQuery provides robust security features including IAM roles, customer-managed encryption keys, and detailed audit logging.
  • Cost Efficiency
    The pricing model is based on the amount of data processed, which can be cost-effective for many use cases when compared to traditional data warehouses.
  • Managed Service
    Being fully managed, BigQuery takes care of database administration tasks such as scaling, backups, and patch management, allowing users to focus on their data and queries.

Possible disadvantages of Google BigQuery

  • Cost Predictability
    While the pay-per-use model can be cost-efficient, it can also make cost forecasting difficult. Unexpected large queries could lead to higher-than-anticipated costs.
  • Complexity
    The learning curve can be steep for those who are not already familiar with SQL or Google Cloud Platform, potentially requiring training and education.
  • Limited Updates
    BigQuery is optimized for read-heavy operations, and it can be less efficient for scenarios that require frequent updates or deletions of data.
  • Query Pricing
    Costs are based on the amount of data processed by each query, which may not be suitable for use cases that require frequent analysis of large datasets.
  • Data Transfer Costs
    While internal data movement within Google Cloud can be cost-effective, transferring data to or from other services or on-premises systems can incur additional costs.
  • Dependency on Google Cloud
    Organizations heavily invested in multi-cloud or hybrid-cloud strategies may find the dependency on Google Cloud limiting.
  • Cold Data Performance
    Query performance might be slower for so-called 'cold data,' or data that has not been queried recently, affecting the responsiveness for some workloads.

Analysis of Google BigQuery

Overall verdict

  • Google BigQuery is a powerful and flexible data warehouse solution that suits a wide range of data analytics needs. Its ability to handle large volumes of data quickly makes it a preferred choice for organizations looking to leverage their data effectively.

Why this product is good

  • Google BigQuery is a fully-managed data warehouse that simplifies the analysis of large datasets. It is known for its scalability, speed, and integration with other Google Cloud services. It supports standard SQL, has built-in machine learning capabilities, and allows for seamless data integration from various sources. The serverless architecture means that users don't need to worry about infrastructure management, and its pay-as-you-go model provides cost efficiency.

Recommended for

  • Businesses requiring fast processing of large datasets
  • Organizations that already utilize Google Cloud services
  • Companies looking for a cost-effective, scalable analytics solution
  • Teams interested in using SQL for data analysis
  • Data scientists integrating machine learning with their data workflows

LightStep videos

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

More videos:

  • Review - Lightstep Chronicles Review

Google BigQuery videos

Cloud Dataprep Tutorial - Getting Started 101

More videos:

  • Review - Advanced Data Cleanup Techniques using Cloud Dataprep (Cloud Next '19)
  • Demo - Google Cloud Dataprep Premium product demo

Category Popularity

0-100% (relative to LightStep and Google BigQuery)
Monitoring Tools
100 100%
0% 0
Data Dashboard
0 0%
100% 100
Application Performance Monitoring
Big Data
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 Google BigQuery

LightStep Reviews

We have no reviews of LightStep yet.
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Google BigQuery Reviews

Database for Data Analytics
Processing typeDescriptionUse casesCommon databasesProcessing typesProcesses data in scheduled intervals (hours, days). High-latency but cost-efficient for large datasets.Financial reporting, trend analysis, historical analyticsSnowflake, Amazon Redshift, Google BigQueryContinuously ingests and processes data with minimal latency for real-time decision-making.Fraud...
Source: blog.devart.com
Data Warehouse Tools
Google BigQuery: Similar to Snowflake, BigQuery offers a pay-per-use model with separate charges for storage and queries. Storage costs start around $0.01 per GB per month, while on-demand queries are billed at $5 per TB processed.
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
You can also use BigQueryโ€™s columnar and ANSI SQL databases to analyze petabytes of data at a fast speed. Its capabilities extend enough to accommodate spatial analysis using SQL and BigQuery GIS. Also, you can quickly create and run machine learning (ML) models on semi or large-scale structured data using simple SQL and BigQuery ML. Also, enjoy a real-time interactive...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Google BigQuery is an incredible platform for enterprises that want to run complex analytical queries or โ€œheavyโ€ queries that operate using a large set of data. This means itโ€™s not ideal for running queries that are doing simple filtering or aggregation. So if your cloud data warehousing needs lightning-fast performance on a big set of data, Google BigQuery might be a great...
Top 5 BigQuery Alternatives: A Challenge of Complexity
BigQuery's emergence as an attractive analytics and data warehouse platform was a significant win, helping to drive a 45% increase in Google Cloud revenue in the last quarter. The company plans to maintain this momentum by focusing on a multi-cloud future where BigQuery advances the cause of democratized analytics.
Source: blog.panoply.io

Social recommendations and mentions

Based on our record, Google BigQuery should be more popular than LightStep. It has been mentiond 47 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.

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

Google BigQuery mentions (47)

  • Ruby on Rails Performance: 7 Lessons from Scaling FirstPromoter
    We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery โ€” we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 3 months ago
  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 4 months ago
  • What if ML pipelines had a lock file?
    Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 5 months ago
  • Best SQL Courses with Certificates for 2026
    SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferabilityโ€”while dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 7 months ago
  • Why Your Snowflake Bill is High and How to Fix It with a Hybrid Approach
    Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 8 months ago
View more

What are some alternatives?

When comparing LightStep and Google BigQuery, 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.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โ€ŽWhat is Apache Spark?

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

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.

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.

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.