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Astrato Analytics vs Sigma Computing: A Comprehensive Comparison for 2024

In today’s data-driven business environment, as you’re replatforming into the cloud, selecting the right cloud-native analytics platform is critical. This post delves into a comprehensive comparison between Astrato Analytics and Sigma Computing, highlighting the strengths and weaknesses of each platform in key areas such as Guided Analytics, Self-Service, Data Apps/Writeback, Reporting, and Data Modeling, as well as commonalities.

Cloud-native Technical Architecture

The Pushdown SQL architecture championed by Astrato Analytics and Sigma Computing marks a notable departure from the in-memory models of legacy BI systems, aligning perfectly with cloud efficiencies. By processing queries directly on the data source, it eliminates the data movement and replication that burden in-memory systems, fully leveraging the cloud’s scalable nature. This approach is inherently more resource-efficient, avoiding the high costs and limitations associated with preloading data into memory. Consequently, it offers businesses a more agile and cost-effective solution, adeptly meeting the demands of large-scale, real-time data analysis in the cloud era.


Astrato Analytics: Pushdown SQL

Sigma Computing: Pushdown SQL

Astrato Analytics and Sigma Computing’s technical architecture are both based on a Pushdown SQL engine, executing SQL against the data source, and presenting results of the query to the user. This comes with a plethora of benefits against traditional legacy BI vendors.

  • Real-time & Live analytics – acceleration of time-value of data, by presenting in live from the data source, without batch load processes
  • Analytics at scale – small or large datasets, concurrent users, whatever the data source provides, gets visualized instantly in both Astrato and Sigma
  • Centralized governance – data security well done once at source, inherited by your analytics layer. With full data lineage, tracing back from chart to source table.
  • New analytics use cases –  from writing data back to the source, to additional advanced analytics workloads such as python/java based ML/AI workloads, executed at source, and surfaced to the user via both Astrato and Sigma

Guided Analytics: A Tale of Two Approaches

Astrato Analytics shines in the realm of guided analytics, offering an exceptional dashboarding and analytics experience. It leverages AI to generate insights, allowing developers to craft applications that reveal hidden insights efficiently. Features like AI-powered insights, familiar filtering experiences, and pixel-perfect designs set Astrato apart, making it a robust tool for data exploration and decision-making.

Conversely, Sigma Computing presents a strong spreadsheet experience but falls short in advanced dashboarding. Its rigid grid layout system and manual cross-visualization filtering highlight its limitations. Although Sigma supports drill-down capabilities, the absence of AI-powered insights and limited design control curtail its effectiveness in guided analytics.

Guided Analytics Comparison:

Astrato Analytics: Worldclass dashboarding & analytics beyond legacy BI vendors.
Astrato’s approach to guided analytics combines a wide range of visualizations, control elements and actions with AI generated insights, enabling developers to build an app that highlights and exposes hidden insights to end-users with a few clicks.

  • AI powered insights, All users can find insights quickly in any dashboard\chart, enhancing data literacy and reading any type of chart.
  • Familiar filtering experience similar to QlikView, Qlik Sense, Tableau
  • Actions, developers can build a guided analytics flow that only requires users to click buttons.
  • Pixel perfect design, developers can apply company design language easily with full white labeling support (customer-facing)
Sigma Computing: Strong spreadsheet experience, but basic dashboarding.
Offers a range of visualizations and controls to build dashboards but with limited design control in the visualizations and sheets with no guided user journeys

  • Inflexible Grid layout system
  • Unusual Filtering is done only on individual visualizations, cross visualizations filtering is defined manually.
    No filter bar that shows in one place all the filters that were applied in the workbook.
  • Drill down, users can drill down to another dimensions to see a detailed view of the visualization.
  • No AI powered insights, limiting developers to use only simple visualizations

Self-Service: Empowering Business Users

Astrato Analytics champions a no-code, consumer-grade experience tailored for business users, facilitating everything from data connection to dashboard creation. Its no-code measure & dimension builder and GA Insight advisors significantly enhance user autonomy and data literacy.

Sigma Computing, catering to Excel enthusiasts, offers a powerful and flexible environment. However, it demands a certain level of SQL understanding and lacks comprehensive filtering interactivity, potentially hindering seamless data exploration for end-users.

Self-service Comparison:

Astrato Analytics: Consumer-grade for Business Users
End-to-end no-code product experience has been developed with the end Business User in mind. From data connection, measure creation, to dashboard generation, supported by GAI, to mass consumption of data across the entire business, regardless the skill level.

  • No-Code Measure & Dimension builder
  • Consumer-grade ad-hoc custom report for dashboards
  • GA Insight advisors across the product
  • (Coming soon, BI Co-pilot, for swift dashboard creation assistance)
Sigma Computing: Powerful and flexible for Excel enthusiasts
Uses a familiar spreadsheet interface to give business users instant access to explore and get insights from their cloud data warehouse.

  • Requires some SQL knowledge and understanding of writing expressions
  • Basic filtering interactivity on dashboard level limits data exploration abilities for end users
  • Targets power users that are spreadsheet experts

Data Apps and Writeback: Functional Flexibility

In the sphere of Data Apps and Writeback, Astrato Analytics outperforms with its ability to deliver dynamic data solutions and sophisticated data manipulation. Its rich UI and comprehensive audit logs support advanced reporting workflows and the creation of intricate data applications.

Sigma’s approach, though suitable for quick data entry and manipulation, is less effective in complex business scenarios, such as finance reporting or data apps requiring detailed audit trails, underscoring its limitations in scalability and depth.

 

Data Apps – Writeback Comparison:

Astrato Analytics: Multi-step workflow Data Apps
Astrato excels in delivering dynamic data solutions, seamlessly blending sophisticated data manipulation with interactive analytics. It stands out for enabling comprehensive finance reporting workflows, advanced forecasting through integration with MLAs & LLMs, the creation of data applications with full audit logs.

  • Astrato’s rich UI enables stakeholders to easily interpret data and act on insights, fostering a proactive approach to decision-making.
  • Astrato gives you the tools to build it your way, without limitations
Sigma Computing: Quick data entry & manipulation
Sigma’s streamlined write-back approach is ideal for swift prototyping only. However, it falls short in handling business scenarios like finance reporting with approval workflows, implementing forecasting and predictions via machine learning algorithms, constructing data apps with comprehensive audit logs.

  • Limited use case support may not fully meet the diverse demands of enterprises.
  • Simple to set up, almost no settings are needed
  • Not suitable for productionised use cases

 

Reporting: Detail and Design

Astrato’s enterprise reporting capabilities excel with pixel-perfect dashboards and AI insights, enabling the creation of detailed and actionable reports. Its flexibility in report customization and the ability to handle large datasets stand out.

Sigma, with its basic checkbox exporting feature, lacks the finesomeness of Astrato’s reporting. The absence of pixel-perfect design and limited multi-page report functionalities restrict its reporting prowess.

Static Reporting PDF/XLS Comparison:

Astrato Analytics: Enterprise Reporting capabilities
Astrato offers pixel perfect dashboards combined with AI Insights automatically summarize data, highlight patterns and pinpoint outliers makes reports richer. With the underlying Pushdown SQL engine reports can be built upon billions of rows using Excel templates.

  • Scheduled or ad-hoc report generation – providing stakeholders with the latest information exactly when needed.
  • Cyclical Reporting
  • Full flexibility to customize the output with Astrato’s pixel-perfect design
Sigma Computing: Checkbox Exporting feature.
Sigma offers reporting based on their workbook designer, not a pixel perfect design, the reporting output is limited there are no loops to create multi page reports out of a single dashboard.

  • No ability to design a dashboard specifically for reporting with pixel perfect placing of objects. Reports are not compelling.
  • Scheduled exports – users can schedule exports with the latest information.
  • No Excel output format
  • No cyclical reporting

Data Modeling: Complexity Made Simple

Astrato again leads in data modeling, accommodating multi-fact data models and offering visual aids with automatic join suggestions. Its no-code approach and reusable metrics layer foster an intuitive and efficient modeling process.

Sigma’s single output dataset approach, while simpler, may lead to analytical inaccuracies, such as fan traps and chasm traps, indicating a need for more robust data modeling capabilities.

 

Data Modeling Comparison:

Astrato Analytics: Support for Multi-fact Data Models.
Astrato SQL data engine is builts to handle real-life multi fact data model. Data modeling in astrato is done in a visual way with automatic join suggestions, with limited restrictions on data model structures. Astrato engine build ad-hoc table for each chart, preventing fan traps and chasm traps.

  • No-code complex measures creation that enables self-service for all end-users.
  • Shareable Data models- enables one source of through for self-service and guided analytics workbooks.
  • Reusable Metrics Layer (one Data Model -> multiple Workbooks)
Sigma Computing: Single Output dataset.
Sigma data modelling is aimed to build a single output dataset that is used in visualizations, this results in falling into fan traps and chasm traps, and might result with showing wrong numbers.

  • Single dataset approach results in falling to fan traps and incorrect numbers especially when end-users perform self-service
  • Datasets can be shared and used as a source for multiple workbooks.
  • Metrics are written manually, and building complex metrics with filters requires knowledge of an expression language.

Is it and either or? Maybe it’s both

The comparison between Astrato Analytics and Sigma Computing highlights their distinctive analytics approaches. Astrato excels with a full suite of features for analytics, while Sigma specializes in spreadsheet-focused data interaction. Yet, the necessity to choose one over the other is obsolete. But does it have it be an either or? Their shared Pushdown SQL architecture means both can efficiently work within a cloud data environment, allowing for a harmonious coexistence. Integrating Astrato’s presentation strengths with Sigma’s spreadsheet prowess, businesses benefit from a versatile analytics toolkit. This strategy lets companies leverage the best aspects of both platforms, creating a robust data ecosystem much like the interplay between Excel and PowerPoint.

 

How Astrato Adds Value to Snowflake

Astrato enables users to unleash the full potential of Snowflake’s efficiency and scalability. By utilizing Snowflake’s elastic scalability and combining it with Astrato’s functionalities, users can cater to diverse workloads without over-provisioning resources.

Astrato allows users to benefit from Snowflake’s Snowpark, where users can create User-Defined Functions (UDFs) in Python, Java or Scala to supplement Snowflake’s SQL interface. UDFs enable custom logic and transformations directly within the Snowflake Data Cloud. This agility ensures insights remain adaptable and responsive to evolving business (and therefore analytical) needs.

Astrato also works fantastically with Snowflake Cortex’s LLM and ML features.