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Looker VS Apache Arrow

Compare Looker VS Apache Arrow and see what are their differences

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Do-It-Yourself Data Analytics & Business Intelligence, Powered by AI featured
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  3. » Reviews
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Looker logo Looker

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

Apache Arrow logo Apache Arrow

Apache Arrow is a cross-language development platform for in-memory data.
  • Looker Landing page
    Landing page //
    2023-10-11

Looker is a business intelligence platform with an analytics-oriented application server that sits on top of relational data stores. The Looker platform includes an end-user interface for exploring data, a reusable development paradigm for creating data discovery experiences, and an extensible API set so the data can exist in other systems. Looker enables anyone to search and explore data, build dashboards and reports, and share everything easily and quickly.

  • Apache Arrow Landing page
    Landing page //
    2021-10-03

Looker features and specs

  • Powerful Data Modeling
    Looker uses LookML, a proprietary modeling language, making it possible to transform raw data into meaningful metrics and dimensions, providing deep insights without needing SQL expertise.
  • Ease of Use
    Its intuitive user interface enables non-technical users to create visualizations and reports with relative ease, reducing the workload on data teams.
  • Customization
    Looker offers extensive customization options for data exploration and visualization, allowing dashboards and reports to be tailored to specific user needs.
  • Embedded Analytics
    Provides robust capabilities for embedding analytics into applications or portals, broadening the scope of data-driven decision-making throughout the organization.
  • Real-time Data
    Supports real-time data analytics by querying live data, which ensures up-to-date insights and helps in making timely decisions.
  • Integrations
    Looker integrates seamlessly with a wide range of databases and cloud data warehouses, including Google BigQuery, Amazon Redshift, and Snowflake.

Possible disadvantages of Looker

  • Learning Curve
    LookML, while powerful, can be complex for beginners who are not already familiar with data modeling or SQL, resulting in a steep learning curve.
  • Cost
    Looker can be expensive, especially for small businesses, as pricing is typically based on the number of users and the data volume processed.
  • Performance
    Query performance can sometimes be slow, especially with complex data models and large data sets, which may impact the user experience.
  • Customization Constraints
    While Looker offers great customization, certain advanced customizations may require significant expertise and time, posing a potential barrier.
  • Limited Offline Capabilities
    Looker is primarily designed for online use, so it lacks robust offline capabilities, which can be a limitation for users who need access to data in situations without internet connectivity.

Apache Arrow features and specs

  • In-Memory Columnar Format
    Apache Arrow stores data in a columnar format in memory which allows for efficient data processing and analytics by enabling operations on entire columns at a time.
  • Language Agnostic
    Arrow provides libraries in multiple languages such as C++, Java, Python, R, and more, facilitating cross-language development and enabling data interchange between ecosystems.
  • Interoperability
    Arrow's ability to act as a data transfer protocol allows easy interoperability between different systems or applications without the need for serialization or deserialization.
  • Performance
    Designed for high performance, Arrow can handle large data volumes efficiently due to its zero-copy reads and SIMD (Single Instruction, Multiple Data) operations.
  • Ecosystem Integration
    Arrow integrates well with various data processing systems like Apache Spark, Pandas, and more, making it a versatile choice for data applications.

Possible disadvantages of Apache Arrow

  • Complexity
    The use of Apache Arrow can introduce additional complexity, especially for smaller projects or those which do not require high-performance data interchange.
  • Learning Curve
    Getting accustomed to Apache Arrow can take time due to its unique in-memory format and APIs, especially for developers who are new to columnar data processing.
  • Memory Usage
    While Arrow excels in speed and performance, the memory consumption can be higher compared to row-based storage formats, potentially becoming a bottleneck.
  • Maturity
    Although rapidly evolving, some Arrow components or language implementations may not be as mature or feature-complete, potentially leading to limitations in certain use cases.
  • Integration Challenges
    While Arrow aims for broad compatibility, integrating it into existing systems may require substantial effort, affecting development timelines.

Analysis of Looker

Overall verdict

  • Looker is generally considered a robust solution for organizations looking to enhance their data-driven decision-making capabilities. Its flexibility, extensibility, and ease of use make it a strong contender in the BI space, though it may require some learning and setup effort to fully utilize its features.

Why this product is good

  • Looker is a data analytics platform that provides powerful tools for data exploration, visualization, and business intelligence. It offers a user-friendly interface and is known for its ability to connect to a wide variety of data sources. Looker's LookML, a modeling language, allows users to define data relationships and calculations, making it easier to create custom reports and dashboards. Additionally, it integrates well with other tools and supports collaboration with data teams.

Recommended for

  • Companies seeking scalable and flexible business intelligence solutions.
  • Organizations that need to integrate multiple data sources.
  • Teams looking for a collaborative platform with custom reporting capabilities.
  • Users who prefer a code-based approach to data modeling and analysis.

Looker videos

+ Add

Looker Review

More videos:

  • Tutorial - How To Use Looker as a Business User
  • Review - Looker Review - Off The Shelf Reviews

Apache Arrow videos

+ Add

Wes McKinney - Apache Arrow: Leveling Up the Data Science Stack

More videos:

  • Review - "Apache Arrow and the Future of Data Frames" with Wes McKinney
  • Review - Apache Arrow Flight: Accelerating Columnar Dataset Transport (Wes McKinney, Ursa Labs)

Category Popularity

0-100% (relative to Looker and Apache Arrow)

Looker

Apache Arrow

Data Dashboard
100 100%
Data Dashboard
0% 0
Databases
0 0%
Databases
100% 100
Business Intelligence
100 100%
Business Intelligence
0% 0
Big Data
0 0%
Big Data
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 Looker and Apache Arrow

Looker Reviews

Explore 7 Tableau Alternatives for Data Visualization and Analysis
Looker Studio, formerly Google Data Studio, is a user-friendly business intelligence tool that transforms raw data into interactive, customizable dashboards and reports. It integrates seamlessly with Google's ecosystem and supports various data sources, including Google Analytics and BigQuery. Looker Studio offers robust visualization capabilities and real-time collaborative...
Source: www.draxlr.com
Explore 6 Metabase Alternatives for Data Visualization and Analysis
To find the best Metabase alternative for your business, start by listing your specific requirements, such as customer support, data integrations, visualization options, user access controls, and budget. Compare these needs with the features of other BI tools like Draxlr, Tableau, Power BI, Looker, or Holistics. Once you've identified a few suitable options, take advantage...
Source: www.draxlr.com
5 best Looker alternatives
In this blog, we’ll dive into the best 5 Looker alternatives currently dominating the market. Whether you're seeking a Looker alternative with enhanced features, better pricing, or a more tailored fit for your analytics needs, this guide will help you discover BI tool that could be a perfect match for your business.
Source: www.draxlr.com
10 Best Alternatives to Looker in 2024
Exploring alternatives to Looker isn't just about finding a different tool; it's about uncovering solutions that better address your specific business challenges and operational workflows. Here, we highlight five areas where Looker's limitations might lead you to consider other options.
Source: www.sigmacomputing.com
6 Best Looker alternatives
So who are Looker’s competitors? Our top 5 Looker alternatives provide data visualisation and exploration for business intelligence but also offer lower price points, less of a learning curve, and more accessibility for your non-tech team.
Source: trevor.io

Apache Arrow Reviews

We have no reviews of Apache Arrow yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Apache Arrow should be more popular than Looker. It has been mentiond 40 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.

Looker mentions (14)

  • edit home page to add folder section
    Then in the "foldername" you can have 5 folders, each one for each of the groups. This means that when group1 enters looker.com, his default page will be the "foldername", which contains group1folder (he cannot see the rest of the folders if you have set the permissions correctly for each folder). Source: over 2 years ago
  • Stars, tables, and activities: How do we model the real world?
    Even if you want to make Wide Tables, combining fact and dimensions is often the easiest way to create them, so why not make them available? Looker, for example, is well suited to dimensional models because it takes care of the joins that can make Kimball warehouses hard to navigate for business users. - Source: dev.to / almost 3 years ago
  • dbt for Data Quality Testing & Alerting at FINN
    We take daily snapshots of test results, aggregate them, and send Looker dashboards to the appropriate teams. - Source: dev.to / over 3 years ago
  • I'm a dev ID 10 T please help me
    Dashboard: I like to use Datastudio because it's easy (just like using google sheets), but you can also try out Looker. Source: almost 4 years ago
  • The Data Stack Journey: Lessons from Architecting Stacks at Heroku and Mattermost
    For Growth and larger, I would recommend Looker. The only reason I wouldn't recommend it for the smaller company stages is that the cost is much higher than alternatives such as Metabase. With Looker, you define your data model in LookML, which Looker then uses to provide a drag-and-drop interface for end-users that enables them to build their own visualizations without needing to write SQL. This lets your... - Source: dev.to / almost 4 years ago
View more

Apache Arrow mentions (40)

  • Show HN: Typed-arrow – compile‑time Arrow schemas for Rust
    I had no idea what Arrow is: https://arrow.apache.org or arrow-rs: https://github.com/apache/arrow-rs. - Source: Hacker News / about 2 months ago
  • Show HN: Pontoon, an open-source data export platform
    - Open source: Pontoon is free to use by anyone Under the hood, we use Apache Arrow (https://arrow.apache.org/) to move data between sources and destinations. Arrow is very performant - we wanted to use a library that could handle the scale of moving millions of records per minute. In the shorter-term, there are several improvements we want to make, like:. - Source: Hacker News / 2 months ago
  • Unlocking DuckDB from Anywhere - A Guide to Remote Access with Apache Arrow and Flight RPC (gRPC)
    Apache Arrow : It contains a set of technologies that enable big data systems to process and move data fast. - Source: dev.to / 10 months ago
  • Using Polars in Rust for high-performance data analysis
    One of the main selling points of Polars over similar solutions such as Pandas is performance. Polars is written in highly optimized Rust and uses the Apache Arrow container format. - Source: dev.to / 11 months ago
  • Kotlin DataFrame ❤️ Arrow
    Kotlin DataFrame v0.14 comes with improvements for reading Apache Arrow format, especially loading a DataFrame from any ArrowReader. This improvement can be used to easily load results from analytical databases (such as DuckDB, ClickHouse) directly into Kotlin DataFrame. - Source: dev.to / over 1 year ago
View more

What are some alternatives?

When comparing Looker and Apache Arrow, you can also consider the following products

Microsoft Power BI - BI visualization and reporting for desktop, web or mobile

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.

Apache Parquet - Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.

Sisense - The BI & Dashboard Software to handle multiple, large data sets.

DuckDB - DuckDB is an in-process SQL OLAP database management system

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