# Best Data Management Software for Small Analytics Teams in 2026 > A layer-by-layer data stack for 1 to 5 person analytics teams: default picks, published prices, and which tools to skip. Source: https://www.erathos.com/en/blog/best-data-management-software-small-analytics-teams-2026 Em português: https://www.erathos.com/blog/best-data-management-software-small-analytics-teams-2026 Published: 2026-10-01 Category: Tool Guides ![Best Data Management Software for Small Analytics Teams](https://cms-media.erathos.com/Best Data Management Software for Small Analytics Teams.png) Most "best data management software" lists are written for companies with a data platform team. They rank governance suites, master data tools, and catalogs that cost more per year than a small team's whole budget. This guide is for the other buyer: an analytics team of one to five people, no dedicated data engineer, that needs data from a handful of tools in one warehouse with dashboards on top. We go layer by layer (ingestion, warehouse, transformation, orchestration, BI), give a default pick for each, show the published prices side by side, and say which layers you can skip at the start. Every price below comes from the vendor's own pricing page as of this writing. Pricing pages change, so check them before you sign. ## What is data management software for a small analytics team? Data management software is the set of tools a team uses to collect, move, store, organize, and use its data. For a small analytics team, that means five layers: an ingestion tool, a warehouse, a transformation tool, a scheduler, and a BI tool. The broad definition is the same one the enterprise lists use. Data management tools help organizations [collect, move, store, clean, govern, and use data](https://estuary.dev/blog/data-management-tools/) across its whole lifecycle. The difference is in what gets bought. Enterprise lists split the market into [data integration, warehousing, governance and catalog, and master data management](https://estuary.dev/blog/data-management-tools/). The last two categories exist to manage data across dozens of teams and systems. A catalog or governance product also does not replace the rest of the stack. Alation, one of the biggest catalog vendors, says its own product [still needs tools like dbt or Snowflake](https://www.alation.com/blog/data-management-software/) for transformation and storage. So a small team buys the storage and transformation layers first, and adds catalog or master data tools later, if ever. One distinction we see trip up buyers is that an integration tool and a data management platform are different things. A data movement tool copies data from source systems into a warehouse. It does not store the data long term, model it, or chart it. We wrote a longer explainer on [what a data movement platform does and does not do](https://www.erathos.com/en/blog/data-movement-platform?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams). ## What is the best starter data stack for a 1 to 5 person analytics team? Our default starter set is managed ingestion (Erathos), BigQuery on on-demand pricing, dbt on the free Developer plan, the schedulers built into those tools instead of a separate orchestrator, and Metabase for dashboards. Four of the five layers have a free tier, so a team can run the whole stack for a few months before paying anyone. ![starter data stack by layer](https://cms-media.erathos.com/starter data stack by layer.png) Layer Default pick Free tier What you pay for when you outgrow it Ingestion [Erathos](https://www.erathos.com/en/pricing?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams) Up to 1M rows/month, 1 user, 5 pipeline jobs, daily updates Rows written per month: $29/mo for 2M rows, $250/mo for 5M rows Warehouse [BigQuery on-demand](https://cloud.google.com/bigquery/pricing) First 1 TiB of query data and 10 GiB of storage per month $6.25 per TiB scanned, plus storage Transformation [dbt Developer](https://www.dbt.com/pricing) 1 seat, 3,000 successful models per month $100 per user/month on Starter Orchestration Built-in schedulers in Erathos and dbt Included Prefect or Dagster once you need cross-tool dependencies BI [Metabase](https://www.metabase.com/pricing) Open source, self-hosted, unlimited users $100/mo hosted Starter with 5 users included This is a recommendation, and the sections below explain what changes it. A team already on Microsoft licenses will pick Power BI over Metabase. A team with an existing dbt project and a bigger budget might pick Lightdash. A team in Brazil paying in reais will look at Kondado. The point of the default is to have something to compare against. ## Which data ingestion and ELT tool should a small team choose? A managed ingestion tool is the right call for a team that does not want to write and maintain connector code. The tools differ mostly in how they charge: Erathos counts rows written, Fivetran counts monthly active rows, Airbyte sells credits, Estuary charges per gigabyte plus per connector, and Nekt charges per pipeline run. ELT stands for extract, load, transform: copy the raw data into the warehouse first, then reshape it with SQL there. For a team without a data engineer, a managed ELT tool handles the tedious parts. It [handles authentication, schema changes, reprocessing, and delivers organized data](https://www.erathos.com/en/blog/data-centralization-without-a-dedicated-team?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams) into the warehouse. Here is how the main options compare on published pricing and limits. Tool Price unit Free tier Entry paid plan Connectors Fastest sync on entry paid plan [Erathos](https://www.erathos.com/en/pricing?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams) Rows written to the warehouse per month 1M rows, 1 user, 5 jobs, daily updates $29/mo: 2M rows, 3 users, unlimited jobs and connectors, hourly updates 141 listed Hourly ($29), every 5 minutes on Pro ($250) [Fivetran](https://www.fivetran.com/pricing) Monthly active rows (MAR) per connection 500,000 MAR for connections Standard: usage-based per connection, plus a $5 base charge per connection between 1 and 1M MAR 700+ 15 minutes [Airbyte Cloud](https://airbyte.com/pricing) Credits (Standard, Plus); Data Workers (Pro, Enterprise) None listed; Airbyte's calculator shows 40 credits at $195/mo on Standard Standard: pay as you go 700+ 1 hour on Standard, 15 minutes on Plus [Estuary Flow](https://estuary.dev/pricing/) GB moved plus connector instances 10 GB/month, 2 connector instances $0.50 per GB plus $100/month per connector instance (first 6), $50 after 200+ Real-time [Kondado](https://kondado.com.br/pricing.html) Records (rows up to 500 bytes) per month 14-day trial R$99/mo: 4 integrations, 1M records, daily Not stated on pricing page Daily on entry plan [Nekt](https://www.nekt.com/pricing) Pipeline runs (credits) 40 runs/mo, 10 GB of bundled warehouse storage, 3 sources and 3 destinations From $149/mo: 1,000 runs, 100 GB storage 300+ Not stated on pricing page The price units matter more than the sticker prices, because they respond to different things. Fivetran's MAR counts [inserts, updates, and deletes](https://www.fivetran.com/pricing), but not the initial load or rows that did not change. A table that gets updated often costs more than a table that only grows. Each Fivetran connection also [follows its own cost curve](https://www.fivetran.com/pricing), so ten small sources can cost more than one big one. Erathos counts [rows written to the warehouse during the month](https://www.erathos.com/en/pricing?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams), with no limit on the number of connectors. What drives that number is the sync type. A full refresh rewrites the whole table on each run. A partial refresh only writes rows changed since the last run, and needs a [primary key and a date or datetime cursor column](https://docs.erathos.com/platform/connections/sync-types) in the source. Partial overwrite is the suggested sync type on the platform, and it is the one that keeps the row count low. ![erathos pricing](https://cms-media.erathos.com/erathos pricing.png) _Erathos plans are priced by rows written to the warehouse per month_ The mechanics of a run are the same for most managed tools. Erathos [extracts from the source, stages the data in a temporary cloud bucket, loads it into the warehouse with a bulk copy command, then deletes the temporary files](https://docs.erathos.com/platform/how-we-move-data). If you want to understand the cursor versus change data capture (CDC, reading the database's own change log) tradeoff before picking a plan, we compared the two in [cursor-based sync vs CDC](https://www.erathos.com/en/blog/cursor-based-sync-vs-change-data-capture?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams). Airbyte is the pick when you want the option to self-host later. Its own advice is to [start on Standard](https://airbyte.com/pricing) for pay-as-you-go billing, and move to Plus at 40 or more credits a month, where the same calculator shows $189 instead of $195. Estuary is the pick when you need data in the warehouse within seconds instead of minutes. Nekt is the pick if you want the warehouse bundled in and do not mind paying per run. ## Which cloud data warehouse should a small team choose? BigQuery on on-demand pricing is our default for a small team because there is no server to size or turn off, and the first 1 TiB of queries and 10 GiB of storage each month are free. Snowflake, MotherDuck, ClickHouse Cloud, and Databricks each win when a specific condition applies. Warehouse Billing unit Free tier Published rate Best when [BigQuery on-demand](https://cloud.google.com/bigquery/pricing) Bytes scanned by queries, plus storage 1 TiB queries and 10 GiB storage per month $6.25/TiB scanned; storage example: 100 GiB for half a month is $1.15 Variable, low query volume and nobody to manage servers [BigQuery editions](https://cloud.google.com/bigquery/pricing) Slot-hours (a slot is a unit of compute) None Standard edition $0.04 per slot-hour in us-central1, billed per second with a 1-minute minimum Steady, heavy query load where a fixed compute price is cheaper [Snowflake](https://docs.snowflake.com/en/user-guide/cost-understanding-compute) Credits per running virtual warehouse None Per-second billing with a 60-second minimum each time a warehouse starts; no credits while suspended Team already has Snowflake skills or a required Snowflake-native tool [MotherDuck](https://motherduck.com/product/pricing/) Storage per GB plus compute Lite: 10 GB storage, 10 compute hours per month, up to 3 users $0.04/GB/month storage; Business $250/org/month plus usage Data fits in tens of GB and you like DuckDB [ClickHouse Cloud](https://clickhouse.com/pricing) Storage per TB plus compute units per hour None on the pricing page Basic: $25.30/TB/month storage, $0.2181 per compute unit-hour, up to 1 TB, scales to zero when idle Fast dashboards over event data [Databricks](https://www.databricks.com/product/pricing) Compute usage billed per second Trial only Varies by compute type and cloud; storage and networking billed by your cloud provider Team also needs Spark or Python notebooks ### How BigQuery on-demand billing works On-demand billing charges for the data your query reads, with a [10 MB minimum per table referenced and per query](https://cloud.google.com/bigquery/pricing). It bills the columns you select, not the rows you get back. Adding a LIMIT clause [does not reduce the bytes billed](https://cloud.google.com/bigquery/pricing). A query that selects three columns from a wide table costs a fraction of one that selects all of them. ![bigquery on-demand billing](https://cms-media.erathos.com/bigquery on-demand billing.png) Storage is priced separately and is cheap at small-team scale. Active storage costs $0.000031507 per GiB-hour, so [100 GiB stored for half a month costs $1.15](https://cloud.google.com/bigquery/pricing). A table or partition that has not changed for 90 days moves to long-term storage, and its price [drops by about half](https://cloud.google.com/bigquery/pricing). ![bigquery on-demand query pricing](https://cms-media.erathos.com/bigquery on-demand query pricing.png) _BigQuery on-demand query pricing: $6.25 per TiB after the first free TiB each month_ ### How partitioning and clustering cut the bill Partitioning splits a table into segments, most often by a date column, so a query with a date filter only reads the matching segments. Clustering sorts the data inside those segments by one or more columns, so a filter on a clustered column lets BigQuery [skip whole blocks](https://cloud.google.com/bigquery/pricing) of the table. Both reduce the bytes scanned, which is the number the bill is based on. Google's pricing page says to [use partitioning and clustering whenever possible](https://cloud.google.com/bigquery/pricing). ![partition pruning in bigquery](https://cms-media.erathos.com/partition pruning in bigquery.png) For an ingestion tool this matters at load time. A raw events table that lands in BigQuery partitioned by event date means every "last 30 days" dashboard query reads 30 partitions instead of the full history. Partitions that stop changing also qualify for the long-term storage discount on their own, since [each partition is judged separately](https://cloud.google.com/bigquery/pricing). ### On-demand versus capacity pricing On-demand pricing has a variable bill and zero setup: you pay per TiB scanned after the free 1 TiB. Capacity pricing (BigQuery editions) has a predictable bill: you pay [$0.04 per slot-hour on the Standard edition](https://cloud.google.com/bigquery/pricing) in us-central1, billed per second with a one-minute minimum, and queries share that fixed pool of compute. For a small team with a few dashboards refreshing a few times a day, on-demand is almost always the cheaper of the two, and it needs no reservation to set up. Snowflake's model is closer to capacity pricing. You create a virtual warehouse of a chosen size, and [credits are billed per second while it runs, with a 60-second minimum](https://docs.snowflake.com/en/user-guide/cost-understanding-compute) each time it resumes. A suspended warehouse uses no credits. Each size step up [about doubles](https://docs.snowflake.com/en/user-guide/cost-understanding-compute) both the compute and the credits per hour. Snowflake's docs do not publish a dollar price per credit, so we do not compare it in dollars here. ## How should a small team transform data? dbt on the free Developer plan is the default for one analyst writing SQL models, and the Starter plan at $100 per user per month is the step up when a team of up to five needs shared seats and lineage. Bruin is the alternative when you want ingestion and transformation in one open-source tool. Transformation is where raw tables from the ingestion tool become clean tables for dashboards. dbt does this with SQL files that reference each other, and it runs them in the right order in the warehouse. Plan Price Seats Successful models per month Includes [dbt Developer](https://www.dbt.com/pricing) Free 1 3,000 Browser IDE, 1 project, job scheduling and monitoring, CI checks [dbt Starter](https://www.dbt.com/pricing) $100 per user/month 5 developer seats 15,000 Catalog lineage, semantic layer with 5,000 queried metrics per month [dbt Enterprise](https://www.dbt.com/pricing) Custom Custom Custom Custom The 3,000 model limit on Developer is a limit on successful model builds, so a project of 30 models run daily uses 900 of them. A project of 30 models run hourly uses 21,600 and needs Starter. Run frequency, not model count, is what pushes a team off the free plan. dbt also sells an optional usage add-on called dbt State at [$0.094 per daily active target table](https://www.dbt.com/pricing) on pay-as-you-go. We read that line before turning it on. Bruin takes a different shape. It [combines ingestion, SQL and Python pipelines, quality checks, and column-level lineage](https://getbruin.com/) in one tool, with an MIT-licensed CLI you can run anywhere and a managed cloud layered on top. Its homepage lists $100 in credits for new accounts but no full cloud price table, so budget for it after a call with them. ## Does a small analytics team need a separate orchestrator? No, at the start. The ingestion tool and dbt each have a built-in scheduler, and a small team's pipeline is usually "load at 6am, run dbt at 7am." A standalone orchestrator earns its place when jobs in different systems depend on each other, when you need backfills over old dates, or when the pipeline includes custom Python. Erathos plans include scheduled updates (daily on Freemium, hourly on Movement, every 5 minutes on Pro) and a [run history](https://www.erathos.com/en/pricing?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams) of 24 hours, 30 days, or full depending on plan. dbt Developer includes [job scheduling and monitoring](https://www.dbt.com/pricing). Together those cover the two scheduled steps most small teams run. When you do need one, the hosted starter tiers are cheap. Orchestrator Free tier Entry paid plan Billing unit [Prefect Cloud](https://www.prefect.io/pricing) Hobby: 2 users, up to 5 deployments, 500 minutes of Prefect Serverless Starter $100/mo: 3 users, 20 deployments, 75 serverless hours, webhooks Flat plan fee [Dagster+](https://dagster.io/pricing) None Solo $10/mo plus $0.040 per credit and $0.010 per serverless minute; Starter $100/mo for up to 3 users Credits (asset materializations plus ops executed) Prefect's Hobby tier is enough to try orchestration on a real pipeline for free. Dagster's Solo plan is the cheaper entry once you want a paid plan with one user, but read the credit definition: [each asset materialization and each op counts](https://dagster.io/pricing), so a pipeline with many small steps burns credits fast. Dagster's Solo and Starter pricing [took effect May 1, 2026](https://dagster.io/pricing), so older blog posts quoting $120 a month are out of date. ## Which BI tool should a small team choose? Metabase is the default: the open-source edition is free with unlimited users if you host it, and the hosted Starter plan is $100 a month with five users included. Power BI Pro at $14 per user per month is the pick when the company already runs on Microsoft 365. Lightdash is the dbt-native option, at $3,000 a month for the hosted plan. BI (business intelligence) is the dashboard and chart layer on top of the warehouse. Its pricing question is who pays for viewers, because in a small company the people reading dashboards outnumber the people building them. Tool Plan Price Users included Extra users [Metabase](https://www.metabase.com/pricing) Open Source, self-hosted Free Unlimited Free [Metabase](https://www.metabase.com/pricing) Starter, hosted $100/mo 5 $6 per user/month [Metabase](https://www.metabase.com/pricing) Pro $575/mo 10 $12 per user/month [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi/pricing) Pro $14 per user/month, paid yearly Per user $14 each; the free account cannot share reports [Power BI](https://www.microsoft.com/en-us/power-platform/products/power-bi/pricing) Premium Per User $24 per user/month, paid yearly Per user $24 each [Lightdash](https://www.lightdash.com/pricing) Open Source, self-hosted Free Unlimited Free [Lightdash](https://www.lightdash.com/pricing) Cloud Pro $3,000/mo Unlimited None Metabase's per-user price counts [both internal team members and embedded users](https://www.metabase.com/pricing), so a customer-facing dashboard changes the math. Power BI Pro has a [1 GB model size limit, 8 refreshes a day, and 10 GB storage per license](https://www.microsoft.com/en-us/power-platform/products/power-bi/pricing); Premium Per User raises those to 100 GB, 48 refreshes, and 100 TB. Lightdash [requires a dbt project](https://www.lightdash.com/pricing), and its 21-day trial only starts once that project is connected and compiled. ## What will this stack cost, and where do bills surprise teams? Adding up a monthly total is not possible without your workload, because the five layers bill in five different units: rows or MAR for ingestion, bytes scanned or slot-hours for the warehouse, seats and model runs for transformation, credits or deployments for orchestration, and seats for BI. What you can do is know which knob moves each bill. Layer What moves the bill Published trap Ingestion Sync frequency and sync type Fivetran adds a [$5 base charge](https://www.fivetran.com/pricing) to every standard connection between 1 and 1M MAR; Estuary charges [$100/month per connector instance](https://estuary.dev/pricing/) on top of GB Warehouse Bytes scanned per query, number of dashboard refreshes BigQuery bills a [10 MB minimum per table per query](https://cloud.google.com/bigquery/pricing) and ignores LIMIT; Snowflake bills a [60-second minimum](https://docs.snowflake.com/en/user-guide/cost-understanding-compute) on every warehouse resume Transformation Runs per day times number of models dbt Developer's [3,000 successful models per month](https://www.dbt.com/pricing) is easy to pass with hourly runs Orchestration Steps per run Dagster counts [every asset materialization and op](https://dagster.io/pricing) as a credit BI Number of viewers Metabase counts [embed users as users](https://www.metabase.com/pricing); Power BI's free account [cannot share reports](https://www.microsoft.com/en-us/power-platform/products/power-bi/pricing) Sync frequency is the knob that touches two bills at once. Moving an hourly sync to every 5 minutes multiplies the number of loads by 12, and if a dashboard queries the freshly loaded table each time, the warehouse bill moves with it. We set the sync frequency to what the dashboards need. The safest way to forecast is to run a real trial with your worst source. Erathos gives [14 days of the Pro plan on signup, plus a two-week test window for each new connection](https://www.erathos.com/en/pricing?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams), and the platform shows a forecast of month-end usage and emails you when the forecast passes the plan limit. Fivetran offers [14 days free per new connection](https://www.fivetran.com/pricing) and a pricing estimator. Two weeks of real syncs tells you more than any calculator. ## Which layers can a small analytics team skip at first? Skip the catalog, the master data management tool, and the standalone orchestrator at the start. Keep ingestion, a warehouse, and BI from day one. Transformation can wait only while you have a handful of stable views and one person writing them. Here is the reasoning behind each skip. A data catalog documents what tables exist and where they came from. With one warehouse, a dozen source systems, and three people who all know the tables by name, we have seen the catalog turn into a document nobody reads. dbt Starter includes [catalog lineage](https://www.dbt.com/pricing), so when you do need it, it may come with a tool you already pay for. Master data management (MDM) reconciles the same customer or product record across many systems into one golden record. That is an enterprise problem. With a small number of sources, a dbt model that joins on email or tax ID does the same job for free. A standalone orchestrator is worth adding when dependencies cross tools. Until then, the ingestion schedule and the dbt job schedule are the orchestrator. Transformation is the layer to be careful about skipping. Dashboards built straight on raw tables work until a source changes a column name, and then every chart breaks at once. A single dbt project with a staging layer is where that change gets fixed once. We would add dbt the first time two dashboards need the same cleaned table. ## How do you choose data management software without buying too much stack? We decide in this order: sources and destination first, then update frequency, then transformations, then who reads the dashboards, and only then orchestration, catalog, or MDM. Each answer narrows the next choice, and the order keeps you from buying an orchestrator before you have anything to orchestrate. 1. List your sources and pick the warehouse. Count the systems you need data from and check they are in the connector list of the ingestion tool you are considering. Erathos lists [141 connectors and destinations including BigQuery, ClickHouse, Databricks, Redshift, Postgres, SQL Server, and Supabase](https://www.erathos.com/en/connectors?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams). 2. Decide how fresh the data must be. Daily is free on most tools. Hourly is the first paid tier on Erathos ($29) and the Standard tier on Airbyte. Every 5 to 15 minutes is a mid tier everywhere. Seconds means Estuary or CDC on a Pro plan. 3. Count the transformations. One person and a few views: BigQuery views or dbt Developer. A team and shared models: dbt Starter. 4. Count the dashboard readers. Fewer than five builders and many viewers: Metabase open source or Starter. Company on Microsoft 365: Power BI Pro per user. 5. We add orchestration, catalog, and MDM only when a specific failure tells us to. Then run a two-week proof of concept with your hardest source, the one with the biggest table or the strangest API. We watch the ingestion tool's usage forecast and the warehouse's bytes-scanned numbers, since those two figures are the real monthly bill. For a step-by-step on building the first pipeline once you have picked the tools, see [how to build and manage a data pipeline](https://www.erathos.com/en/blog/how-to-build-and-manage-data-pipeline?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams). ## Which software is best for data management? There is no single best data management software, because the category covers at least five different jobs: moving data, storing it, transforming it, cataloging it, and reconciling master records. The best pick for a small analytics team is a set: a managed ingestion tool, a pay-per-query warehouse, dbt, and an open-source BI tool. For a team of one to five, the combination above costs nothing until the data volume passes 1M rows a month, 1 TiB of queries a month, or one dbt seat. Past that, the first paid steps are $29 a month for ingestion and $100 per user for dbt. ## What are some popular data management tools? The popular tools sort by job. Ingestion: Erathos, Fivetran, Airbyte, Estuary, Kondado, Nekt. Warehouse: BigQuery, Snowflake, Databricks, MotherDuck, ClickHouse Cloud. Transformation: dbt, Bruin. Orchestration: Prefect, Dagster. BI: Metabase, Power BI, Lightdash. Catalog and governance: Alation, Collibra. Master data management: Informatica, Profisee. The last two rows are the ones that dominate enterprise lists and that a small analytics team rarely needs in its first year. This guide grades the rest on small-team fit: free tier, entry price, and how the billing unit behaves. ## Is Excel a data management software? Excel can organize and analyze data, so it qualifies under the broad definition, but it cannot replace an ingestion tool and warehouse once several systems need to be combined on a schedule. A spreadsheet has no connector to your CRM, no history of what changed, no alert when a load fails, and a row limit a single event export can pass. The switch point is repetition. If someone exports a CSV from two systems and pastes them into a workbook once a month, Excel is fine. If that happens weekly, or a third system joins, a managed pipeline into a warehouse is cheaper in hours than the manual work. The [data centralization guide for teams without a dedicated data engineer](https://www.erathos.com/en/blog/data-centralization-without-a-dedicated-team?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams) walks through that switch step by step. ## Try the ingestion layer free The fastest way to test this stack is to connect your hardest source and watch the row count for two weeks. [Try Erathos free for 14 days](https://app.erathos.com/signup?utm_source=blog&utm_medium=organic&utm_content=bydefault&utm_campaign=best-data-management-software-small-analytics-teams): you get the Pro plan for the trial, every new connection gets its own two-week test window, and the usage forecast tells you which plan you would land on before you pay for anything.