Best Big Data Software

Starburst is an enterprise intelligence platform built on Trino and Iceberg, offering an open federated lakehouse that queries data across hybrid environments, available as a managed service on major clouds or as self-managed Starburst Enterprise for private cloud, hybrid and on-premises deployment.

Federation is the core idea and it inverts the usual approach. The conventional answer to scattered data is to copy everything into a central warehouse, which means building and maintaining pipelines, accepting that the copy is always somewhat stale, and paying to store the same data twice. Querying it where it already lives removes the pipeline and the duplication, at the cost of query performance depending on systems the platform does not control.

No lock-in and no replatforming is stated explicitly and it is credible here rather than rhetorical, because the foundations are open source: Trino as the query engine and Iceberg as the table format are both projects a customer could run without Starburst. That is a materially different position from a proprietary warehouse where the data format itself is the lock-in.

Pricing is credit-based and published, which is unusual at this end of the market. The free tier costs nothing forever with up to three clusters and standard execution for ad hoc queries, then tiers start at 0.50 dollars per credit adding flexible execution modes, streaming ingest and advanced cluster management, 0.75 dollars per credit with early access features, and 1.00 dollars per credit with compliance tooling and the highest uptime guarantees. AIDA, the conversational analytics layer, bills token usage separately.

Cloudera Data Platform is a hybrid data and AI platform positioned around running data workloads anywhere, spanning an object store, cloud-native data services, Cloudera AI with AI inference, and managed clusters running Apache Spark, Hive, Impala and HBase, priced by consumption.

The anywhere positioning is the commercial substance and it identifies the customer precisely. Cloudera's proposition is a consistent platform across on-premise data centres, private cloud and public cloud, which matters to a specific and substantial set of organisations.

Those are organisations that cannot simply move everything to a public cloud. Banks, insurers, healthcare providers, government bodies and telecommunications operators hold data under regulatory, sovereignty or contractual constraints that keep it on infrastructure they control, while also wanting to use cloud services where they are permitted. That leaves them running both, and the risk is ending up with two entirely separate data platforms and two teams who cannot help each other.

A single platform spanning both is the argument, and it is a real one, provided the consistency is genuine rather than a shared brand across products that behave differently.

Packaging the open source stack with support is the other half. Spark, Hive, Impala and HBase are all freely available, and an organisation can run them itself. What it cannot easily do is integrate them, secure them consistently, upgrade them without breaking things, and hold somebody accountable when a cluster fails at month end. That accountability is what is actually being bought.

Consumption pricing is published at rates including $0.04, $0.07, $0.08 and $0.20 per unit across data engineering, data flow, application services and managed clusters, which suits variable workloads better than capacity licensing and makes cost control an operational discipline.

Cloudera AI with AI inference extends the platform toward model serving on the same governed data.

Snowflake is a cloud-based data platform that separates storage, compute, and cloud services into independent layers, letting organizations store structured, semi-structured, and unstructured data and run analytics, data engineering, and AI workloads across AWS, Azure, and Google Cloud. It offers editions ranging from Standard to Virtual Private Snowflake, with features like Snowpipe streaming ingestion, Snowpark for Python and Java development, and Snowflake Cortex for building AI and LLM applications on governed data.

Pricing is consumption-based rather than flat-rate, billed per second for compute credits plus separate storage and data transfer charges that vary by edition, cloud, and region. Snowflake does not publish exact per-credit dollar amounts on its main pricing page, directing prospects to a pricing calculator and consumption tables instead, and offers capacity commitment contracts for discounted rates.

Databricks is a cloud-based data and AI platform built around the lakehouse architecture, combining data engineering, data warehousing, machine learning, and generative AI in one workspace. Core components include Unity Catalog for unified governance, Delta Lake for open table storage, MLflow for the ML lifecycle, and Mosaic AI tooling for building and deploying generative AI applications, all running on AWS, Azure, or Google Cloud.

Billing follows a pay-as-you-go model measured in Databricks Units (DBUs) consumed per second, with no upfront cost or fixed contract required, alongside separate cloud infrastructure charges from AWS, Azure, or GCP. Premium is now the baseline tier for new workspaces, since the older Standard tier has been retired on AWS and GCP and restricted on Azure. A no-cost Free Edition is available for individual learning, and committed-use discounts are available for larger organizations.