1.avif)
If you need a BigQuery alternative for Data Studio that is easier for a marketing team to operate, Atlas offers a focused option. It brings Dataslayer data into a controlled marketing data warehouse, preserves familiar reporting workflows, and can run in a managed environment or on customer-controlled servers in a supported region selected by the organization.
Data Studio was known as Looker Studio between 2022 and April 2026. Existing reports continue to work, and searches for a "BigQuery alternative for Looker Studio" refer to the same reporting product under its former name (Google's rebrand announcement).
In short, Atlas is an all-in-one marketing intelligence product: the analytical capabilities marketing teams need, AI-assisted dashboard creation in a few clicks, connected data, and a simpler cost structure than coordinating multiple reporting tools separately.
What is Atlas Business Intelligence?
Atlas is a marketing data warehouse and analytics product integrated with Dataslayer. It is designed for agencies and in-house teams that need reliable advertising, analytics, CRM, social, and e-commerce data without the operational scope of a general-purpose cloud data platform.
Think of it as BigQuery's smaller, marketing-focused sibling. BigQuery is broad and suitable for data workloads across an entire organization. Atlas concentrates on the path marketers use every day: connect campaign data, preserve its history, separate clients or brands, manage access, discover important changes, and deliver trusted reports.
The workflow is straightforward:
Dataslayer's official catalog includes more than 60 marketing connectors for advertising, analytics, social, CRM, and e-commerce data (Dataslayer connector catalog). Atlas adds the organized, governed data layer used for repeated reporting and analysis.
Atlas vs. BigQuery for Data Studio
Google provides a native connection between BigQuery and Data Studio. It requires a Google Cloud billing account, and opening or refreshing reports can generate BigQuery usage costs (Google's official documentation).
That model is valuable when a data team already manages cloud costs, access, and reporting assets. Atlas offers a more guided experience when marketing is the primary use case.
The difference is not whether both products can store data. It is how much specialist administration the marketing team must coordinate around that data.
Why marketing teams look for a BigQuery alternative
Cost and access require active management
BigQuery's on-demand model charges according to the amount of data processed by queries. Google currently includes the first TiB processed each month and lists a price starting at $6.25 per additional TiB, depending on region and currency (BigQuery pricing).
This can be economical, particularly at low usage. As reporting grows, someone still needs to monitor consumption, credentials, and access. A shared report can involve permissions for the dashboard, its source, and the underlying cloud account. For teams without a dedicated data specialist, that coordination can become a distraction from campaign analysis and client delivery.
General-purpose platforms still need marketing context
Marketing teams usually want a specific outcome: trusted spend, impressions, clicks, conversions, revenue, and ROAS, ready to analyze by account, campaign, channel, country, and date.
A broad platform can support this, but it does not automatically decide how clients should be separated, which reporting assets belong together, or who can change them. Atlas makes those marketing workflows part of the product.
Why AI needs a living marketing data warehouse
A static report is a snapshot. It shows what performance looked like when the data was exported, but it cannot continuously place today's change in the context of a complete history.
AI-assisted statistical analysis is most useful when it can compare consistent data across time, channels, campaigns, markets, and business outcomes. A marketing data warehouse creates that shared history and keeps it available for repeated analysis.
Combined data reveals trends that isolated reports miss
A platform report may show that conversions increased. Combined data can reveal that acquisition cost rose faster, that revenue growth came from one country, or that a high-spend channel began underperforming after a campaign change.
The advantage is not simply having more rows. It is comparing consistent business definitions across sources and periods. That makes cross-channel trends, unusual movements, concentration risks, and performance gaps easier to identify.
AI accelerates opportunity and problem discovery
Atlas uses validated analytical signals to detect patterns such as trends, anomalies, drivers, underperforming segments, correlations, and meaningful combinations of dimensions. AI then explains that evidence in business language without changing the underlying figures.
This can direct attention toward questions such as:
- Where is spend increasing without a proportional improvement in results?
- Which market or channel presents the strongest efficiency opportunity?
- Which campaign is moving differently from its recent trend?
- Are conversions growing while revenue quality or ROAS declines?
AI does not replace marketing judgment. It helps analysts examine more combinations and surface relevant changes faster. People still provide business context, validate findings, and decide what action to take.
A marketing data warehouse built for daily work
Workspaces and business roles
Agencies can create dedicated workspaces for individual clients. In-house teams can apply the same model to brands, markets, or business units. Reporting data, dashboards, saved work, and access remain in the correct context.
Atlas provides administrator, editor, and viewer access at warehouse level, plus owner, admin, editor, and viewer roles within each workspace. One person can therefore have different responsibilities for different clients.
Visible usage and activity
Administrators can see warehouse usage and receive alerts at 80%, 90%, and 100% of the plan limit. If the limit is reached, existing data and reports remain available while new additions subject to the limit are paused.
Depending on the subscription, activity history can be retained for 30 or up to 365 days. Marketing leaders gain visibility into data consumption and important user actions.
Customer-controlled deployment
Atlas can be installed on customer-controlled servers in a supported country or region selected by the organization. This self-hosted option is relevant when security policies, data residency, client contracts, or regulatory requirements call for a dedicated environment.
For agencies, deployment flexibility can also strengthen proposals for clients with strict governance requirements. The final location and model remain subject to the customer's environment and implementation requirements.
Import data and connect existing tools
Atlas provides several guided ways to introduce data. Teams can select a Dataslayer source, upload a file, synchronize a spreadsheet, or connect an existing business system without creating a separate reporting process for every dataset.
Users can review a preview before making imported data available. Updates can then be scheduled hourly, daily, or weekly where the selected source supports that frequency. Connector status and processed-row information make data freshness visible rather than leaving it as an unknown background dependency.
A guided BigQuery data migration without starting from zero
Teams that already store marketing data in BigQuery do not need to abandon their history or rebuild the migration table by table. Atlas includes a guided BigQuery import designed to make the transition easier:
- Authorize read access to the relevant BigQuery project.
- Validate the connection and load the available datasets and tables.
- Search the catalog and select the tables or views to migrate.
- Choose the Atlas workspace that should receive each imported asset.
- Import the selected data and schedule future updates when required.
This gives agencies and marketing teams a practical way to preserve historical campaign data while moving daily warehouse management into Atlas. Multiple tables can be selected in the same import flow, and recognizable local names make the migrated data easier to use inside each client workspace.
The change can also be gradual. Existing Data Studio reports can continue using BigQuery while the imported data is validated in Atlas. Once the team confirms that the required history, metrics, and refreshes are available, it can connect new or existing reporting assets to the corresponding Atlas data. Report connections are updated deliberately; Atlas does not claim to rewrite every Data Studio report automatically.
For teams comparing alternatives, this migration path is important: choosing a marketing-focused warehouse does not mean losing the investment already made in BigQuery data collection and historical reporting.
Supported input options
- Dataslayer marketing sources
- Microsoft Excel, CSV, XML, and Google Sheets
- MySQL, PostgreSQL, Microsoft SQL Server, MongoDB, and BigQuery
- Snowflake and Databricks
- Amazon S3, Google Cloud Storage, and Microsoft Azure storage
- REST APIs, webhooks, and Airtable
Availability depends on the customer's plan, credentials, source, and implementation. These options make it possible to combine campaigns with targets, budgets, CRM outcomes, product data, and historical exports.
Reporting and analysis destinations
- Data Studio: interactive reports for teams and clients.
- Microsoft Power BI: broader business intelligence reporting.
- Microsoft Excel and Google Sheets: planning and ad hoc analysis.
- CSV: portable data for established workflows.
- Data API: approved data for customer applications and external consumers.
- Atlas dashboards: shared metrics and AI-assisted analysis in the same product
Atlas can therefore serve as a controlled marketing data layer without forcing every user into the same reporting interface.
When to choose Atlas instead of BigQuery
Atlas is a strong fit when:
- marketing data is the main priority
- the team already uses Dataslayer and Data Studio
- an agency manages multiple clients or brands
- there is no dedicated specialist to administer the warehouse
- product-level roles, usage alerts, and activity history are important
- policies require a private deployment in a supported region
BigQuery may be the better fit when:
- the company needs a general-purpose data platform for many departments
- a mature data team already manages cloud access, costs, and governance
- the use case extends far beyond marketing reporting
- deep integration with the broader Google Cloud ecosystem is essential
- maximum platform flexibility is the main requirement
Atlas is not positioned as a replacement for every BigQuery workload. It is a BigQuery alternative for marketing data when specialization and operational simplicity matter more than the scope of a global cloud platform.
How to evaluate reporting efficiency
A pilot should compare real indicators before and after introducing Atlas:
This evidence can show whether Atlas reduces setup time, specialist intervention, and tool switching. It is more credible than promising the same saving for every organization.
Frequently asked questions
Does Atlas replace Data Studio?
Not necessarily. Atlas can organize and control the data used by Data Studio while also providing its own dashboards and analysis.
Does Atlas replace Dataslayer?
No. Dataslayer connects and moves marketing data. Atlas organizes, governs, and prepares it for repeated analysis and reporting.
Can Atlas migrate our existing BigQuery data?
Yes. After the organization authorizes read access, Atlas can validate the BigQuery connection, display available datasets and tables, import the selected assets into a workspace, and schedule future updates. Teams can migrate in phases while their existing Data Studio reports continue operating against BigQuery until the new reporting connections are ready.
Does Atlas use AI to analyze marketing performance?
Yes. Atlas can detect validated trends, anomalies, underperforming segments, correlations, and opportunities, then explain the evidence in business language. The goal is faster discovery, not automated decision-making without human review.
Can Atlas connect with Excel, Google Sheets, Power BI, and Data Studio?
Yes. Atlas can make reporting data available through these familiar tools, as well as CSV workflows, its own dashboards, and external applications through a data API. Availability can depend on the customer's plan and setup.
Do marketers need SQL skills to use Atlas?
No SQL knowledge is required to view dashboards or use reporting assets that have already been prepared. Advanced options remain available for analysts.
Is Atlas a complete replacement for BigQuery?
Atlas is an alternative for managing and reporting on marketing data. It does not attempt to cover every scenario supported by a general-purpose cloud data platform.
Can Atlas run on our own servers?
Yes. Atlas can be deployed on customer-controlled servers in a supported region selected by the organization, subject to its environment and implementation requirements.
Make marketing data easier to manage
The best warehouse is not necessarily the product with the longest feature list. It is the one that removes the most friction from the team's real work.
Atlas brings the marketing workflow together: connect and import sources, preserve history, discover trends and opportunities with AI, create dashboards in a few clicks, and continue delivering data through Data Studio, Power BI, Excel, Google Sheets, and other established tools.
