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.

Dremio is a lakehouse platform published in three editions: Dremio Cloud as a fully managed platform for agentic AI, Dremio Enterprise as self-managed software running on Kubernetes on-premise or in cloud, and Dremio Community Edition as a free query engine for local machines or servers, with an AI agent, a semantic layer, an intelligent query engine and an open catalogue.

The semantic layer is the capability that distinguishes this from a query engine alone, and it addresses the gap that opens once a lakehouse actually works.

That gap is a familiar one. Once data is queryable directly in object storage, analysts can reach it, but what they find is raw: physical table names, technical column names, and no indication of which of four similar tables is the one people actually use. Being able to query everything is not the same as being able to understand it, and the usual result is that a small number of people who know the layout become a bottleneck for everyone else.

A semantic layer presents business-meaningful views over that physical data, so an analyst works with concepts rather than storage layout, and definitions live in one governed place rather than being re-derived in every query.

Publishing a free community edition for local machines is genuinely useful rather than a marketing gesture, because it lets an engineer evaluate the engine against their own data on their own laptop before any commercial conversation, which in this category tells you far more than a demonstration.

The three-edition structure covers the real deployment constraint. Cloud suits organisations without infrastructure preference, Enterprise on Kubernetes suits those who must keep data in their own environment, and both run the same engine.

Pricing is published as a consumption model with a $0.20 rate for the managed agentic lakehouse on AWS, with Azure noted as coming soon, and a $400 figure published within the range.

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.

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.

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.

Google BigQuery is a serverless data platform positioned as an autonomous data to AI system, automating the data life cycle from ingestion through to AI-driven insight, with Gemini features now included in the standard pricing models.

Serverless is the architectural property everything else follows from, and it changed what a data warehouse costs to own. Conventional warehouses require capacity to be provisioned and paid for continuously, which means an organisation sizes for its heaviest query and pays for that around the clock. BigQuery separates storage from compute and charges for bytes processed, so an analytics workload that runs for two hours a day costs what two hours cost.

The corollary deserves stating just as plainly, because it is where teams get caught: a badly written query scanning an entire table costs real money every time somebody runs it, and a dashboard refreshing every minute against unpartitioned data can produce a genuinely alarming bill. Partitioning, clustering and reviewing query cost are operational disciplines rather than optimisations here.

The free monthly allowance is substantial enough to be useful rather than symbolic, at 10 GiB of stored data and up to 1 TiB of queries every month, which covers a small analytics workload indefinitely. Billing beyond that is usage-based through the Google Cloud pricing calculator rather than plan tiers. Note that the 300 dollar figure on Google Cloud pages is new-customer credit and the 350,000 dollar figure is a startup programme, neither being a BigQuery rate.

Apache Spark is a unified engine for large-scale data analytics, described by the project as a multi-language engine for executing data engineering, data science and machine learning on single-node machines or clusters, with Spark Connect clients published for Go, Rust and Swift alongside a Kubernetes operator and Docker images.

The unified engine framing is the substance of why Spark displaced what came before it, and it is worth understanding what it replaced.

Hadoop's original processing model required expressing every computation as a map and reduce pair, which worked for straightforward batch transformations and became painful for anything iterative. Machine learning training, graph algorithms and interactive exploration all involve passing over the same data repeatedly, and each pass meant writing intermediate results to disk and reading them back. That made a class of work impractically slow rather than merely inconvenient.

Spark kept data in memory between operations, which changed those workloads by orders of magnitude and made interactive analysis on large datasets possible for the first time.

The multi-language support is more consequential than a feature list suggests. Data engineers generally work in Scala or Java, data scientists in Python or R, and analysts in SQL. An engine addressable from all of them means one processing platform rather than a translation layer between teams, and it removes the common failure where a model developed in Python has to be rewritten by an engineer for production.

Running on a single node as well as a cluster matters for development practice, since the same code can be written and tested on a laptop before running against the full dataset.

The Kubernetes operator reflects where deployment has moved, with Spark increasingly run on general container infrastructure rather than on a dedicated Hadoop cluster.

Spark is free and open source under the Apache licence, with managed offerings from every major cloud provider.

Apache Hadoop is an Apache Software Foundation project developing open source software for reliable, scalable, distributed computing, published across maintained release lines including 3.4.0, 3.3.6 and 2.10.2, with a wiki, community bylaws, criteria for committership, mailing lists and published security advisories.

Hadoop's historical significance is that it made distributed data processing accessible on commodity hardware, and the ideas it established remain the foundation of the field even as the specific implementation has been displaced for many workloads.

The core insight was to move computation to the data rather than data to the computation. When a dataset is too large for one machine, the conventional approach of loading it into a processing system fails on the transfer alone. Hadoop distributed the data across many machines and ran the processing on the machine holding each piece, which is why it scaled where previous approaches could not.

An honest assessment of where it now sits is more useful than a feature list. Cloud object storage and separated compute engines have replaced Hadoop for a great many workloads that would once have required it, because storing data in object storage and running a query engine against it is simpler and cheaper than operating a cluster where storage and compute are bound together.

Where Hadoop remains relevant is substantial nonetheless. Large existing deployments represent significant investment and continue to run. Organisations that cannot use public cloud for regulatory or sovereignty reasons still need on-premise distributed processing. And the ecosystem that grew around Hadoop, including formats and schedulers, remains in wide use well beyond Hadoop clusters themselves.

Published CVEs and a formal security process are worth noting for anyone running it, since a distributed system holding an organisation's data warrants the same patching discipline as any other infrastructure, and older release lines eventually stop receiving fixes.

The software is free and open source under the Apache licence, with commercial distributions and support available from third parties.