Compare the Top Data Lakehouse Platforms in 2025

Data lakehouse platforms are a type of data architecture that combines a data lake with a data warehouse. It is specifically designed to bring together data from a variety of sources, including relational databases, Hadoop clusters, streaming data sources, and object stores. Data lakehouse platforms enable organizations to store and analyze large volumes of data quickly and cost effectively. They provide an environment for data scientists and data engineers to work together to bring data from multiple sources into a single repository, allowing for easier data exploration and analytics. Furthermore, data lakehouse platforms are able to scale up and down depending on the needs of the organization, making them a flexible and cost-effective solution. Additionally, they can be integrated with other software applications to ensure that the data is always up to date. Finally, they provide an easy-to-use environment for data analysis, making it easier to identify trends and insights. Here's a list of the best data lakehouse platforms:

  • 1
    AnalyticsCreator

    AnalyticsCreator

    AnalyticsCreator

    Optimize your data lakehouse environment with AnalyticsCreator. Automate data ingestion and transformation processes for platforms like Delta Lake, Databricks Lakehouse, and Azure Synapse Analytics, enhancing scalability for real-time and batch processing. Handle diverse data formats while ensuring quality, consistency, and governance within your lakehouse ecosystem. Leverage AnalyticsCreator’s tools to accelerate analytics through automated workflows, providing an ideal solution for modern data challenges.
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  • 2
    Scalytics Connect
    Scalytics Connect enables AI and ML to process and analyze data, makes it easier and more secure to use different data processing platforms at the same time. Built by the inventors of Apache Wayang, Scalytics Connect is the most enhanced data management platform, reducing the complexity of ETL data pipelines dramatically. Scalytics Connect is a data management and ETL platform that helps organizations unlock the power of their data, regardless of where it resides. It empowers businesses to break down data silos, simplify access, and gain valuable insights through a variety of features, including: - AI-powered ETL: Automates tasks like data extraction, transformation, and loading, freeing up your resources for more strategic work. - Unified Data Landscape: Breaks down data silos and provides a holistic view of all your data, regardless of its location or format. - Effortless Scaling: Handles growing data volumes with ease, so you never get bottlenecked by information overload
    Starting Price: $0
  • 3
    Snowflake

    Snowflake

    Snowflake

    Snowflake is a comprehensive AI Data Cloud platform designed to eliminate data silos and simplify data architectures, enabling organizations to get more value from their data. The platform offers interoperable storage that provides near-infinite scale and access to diverse data sources, both inside and outside Snowflake. Its elastic compute engine delivers high performance for any number of users, workloads, and data volumes with seamless scalability. Snowflake’s Cortex AI accelerates enterprise AI by providing secure access to leading large language models (LLMs) and data chat services. The platform’s cloud services automate complex resource management, ensuring reliability and cost efficiency. Trusted by over 11,000 global customers across industries, Snowflake helps businesses collaborate on data, build data applications, and maintain a competitive edge.
    Starting Price: $2 compute/month
  • 4
    Amazon Athena
    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL. Most results are delivered within seconds. With Athena, there’s no need for complex ETL jobs to prepare your data for analysis. This makes it easy for anyone with SQL skills to quickly analyze large-scale datasets. Athena is out-of-the-box integrated with AWS Glue Data Catalog, allowing you to create a unified metadata repository across various services, crawl data sources to discover schemas and populate your Catalog with new and modified table and partition definitions, and maintain schema versioning.
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    Azure Synapse Analytics
    Azure Synapse is Azure SQL Data Warehouse evolved. Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless or provisioned resources—at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.
  • 6
    Teradata VantageCloud
    Teradata VantageCloud is a comprehensive cloud-based analytics and data platform that allows businesses to unlock the full potential of their data with unparalleled speed, scalability, and operational flexibility. Engineered for enterprise-grade performance, VantageCloud supports seamless AI and machine learning integration, enabling organizations to generate real-time insights and make informed decisions faster. It offers deployment flexibility across public clouds, hybrid environments, or on-premise setups, making it highly adaptable to existing infrastructures. With features like unified data architecture, intelligent governance, and optimized cost-efficiency, VantageCloud helps businesses reduce complexity, drive innovation, and maintain a competitive edge in today’s data-driven world.
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    Archon Data Store

    Archon Data Store

    Platform 3 Solutions

    Archon Data Store™ is a powerful and secure open-source based archive lakehouse platform designed to store, manage, and provide insights from massive volumes of data. With its compliance features and minimal footprint, it enables large-scale search, processing, and analysis of structured, unstructured, & semi-structured data across your organization. Archon Data Store combines the best features of data warehouses and data lakes into a single, simplified platform. This unified approach eliminates data silos, streamlining data engineering, analytics, data science, and machine learning workflows. Through metadata centralization, optimized data storage, and distributed computing, Archon Data Store maintains data integrity. Its common approach to data management, security, and governance helps you operate more efficiently and innovate faster. Archon Data Store provides a single platform for archiving and analyzing all your organization's data while delivering operational efficiencies.
  • 8
    Amazon Redshift
    More customers pick Amazon Redshift than any other cloud data warehouse. Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. Companies like Lyft have grown with Redshift from startups to multi-billion dollar enterprises. No other data warehouse makes it as easy to gain new insights from all your data. With Redshift you can query petabytes of structured and semi-structured data across your data warehouse, operational database, and your data lake using standard SQL. Redshift lets you easily save the results of your queries back to your S3 data lake using open formats like Apache Parquet to further analyze from other analytics services like Amazon EMR, Amazon Athena, and Amazon SageMaker. Redshift is the world’s fastest cloud data warehouse and gets faster every year. For performance intensive workloads you can use the new RA3 instances to get up to 3x the performance of any cloud data warehouse.
    Starting Price: $0.25 per hour
  • 9
    iomete

    iomete

    iomete

    Modern lakehouse built on top of Apache Iceberg and Apache Spark. Includes: Serverless lakehouse, Serverless Spark Jobs, SQL editor, Advanced data catalog and built-in BI (or connect 3rd party BI e.g. Tableau, Looker). iomete has an extreme value proposition with compute prices is equal to AWS on-demand pricing. No mark-ups. AWS users get our platform basically for free.
    Starting Price: Free
  • 10
    BigLake

    BigLake

    Google

    BigLake is a storage engine that unifies data warehouses and lakes by enabling BigQuery and open-source frameworks like Spark to access data with fine-grained access control. BigLake provides accelerated query performance across multi-cloud storage and open formats such as Apache Iceberg. Store a single copy of data with uniform features across data warehouses & lakes. Fine-grained access control and multi-cloud governance over distributed data. Seamless integration with open-source analytics tools and open data formats. Unlock analytics on distributed data regardless of where and how it’s stored, while choosing the best analytics tools, open source or cloud-native over a single copy of data. Fine-grained access control across open source engines like Apache Spark, Presto, and Trino, and open formats such as Parquet. Performant queries over data lakes powered by BigQuery. Integrates with Dataplex to provide management at scale, including logical data organization.
    Starting Price: $5 per TB
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    Stackable

    Stackable

    Stackable

    The Stackable data platform was designed with openness and flexibility in mind. It provides you with a curated selection of the best open source data apps like Apache Kafka, Apache Druid, Trino, and Apache Spark. While other current offerings either push their proprietary solutions or deepen vendor lock-in, Stackable takes a different approach. All data apps work together seamlessly and can be added or removed in no time. Based on Kubernetes, it runs everywhere, on-prem or in the cloud. stackablectl and a Kubernetes cluster are all you need to run your first stackable data platform. Within minutes, you will be ready to start working with your data. Configure your one-line startup command right here. Similar to kubectl, stackablectl is designed to easily interface with the Stackable Data Platform. Use the command line utility to deploy and manage stackable data apps on Kubernetes. With stackablectl, you can create, delete, and update components.
    Starting Price: Free
  • 12
    Actian Avalanche
    Actian Avalanche is a fully managed hybrid cloud data warehouse service designed from the ground up to deliver high performance and scale across all dimensions – data volume, concurrent user, and query complexity – at a fraction of the cost of alternative solutions. It is a true hybrid platform that can be deployed on-premises as well as on multiple clouds, including AWS, Azure, and Google Cloud, enabling you to migrate or offload applications and data to the cloud at your own pace. Actian Avalanche delivers the best price-performance in the industry outof-the-box without DBA tuning and optimization techniques. For the same cost as alternative solutions, you can benefit from substantially better performance or chose the same performance for significantly lower cost. For example, Avalanche provides up to 6x the price-performance advantage over Snowflake as measured by GigaOm’s TPC-H industry standard benchmark and even more against many of the appliance vendors.
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    DataLakeHouse.io

    DataLakeHouse.io

    DataLakeHouse.io

    DataLakeHouse.io (DLH.io) Data Sync provides replication and synchronization of operational systems (on-premise and cloud-based SaaS) data into destinations of their choosing, primarily Cloud Data Warehouses. Built for marketing teams and really any data team at any size organization, DLH.io enables business cases for building single source of truth data repositories, such as dimensional data warehouses, data vault 2.0, and other machine learning workloads. Use cases are technical and functional including: ELT, ETL, Data Warehouse, Pipeline, Analytics, AI & Machine Learning, Data, Marketing, Sales, Retail, FinTech, Restaurant, Manufacturing, Public Sector, and more. DataLakeHouse.io is on a mission to orchestrate data for every organization particularly those desiring to become data-driven, or those that are continuing their data driven strategy journey. DataLakeHouse.io (aka DLH.io) enables hundreds of companies to managed their cloud data warehousing and analytics solutions.
    Starting Price: $99
  • 14
    Onehouse

    Onehouse

    Onehouse

    The only fully managed cloud data lakehouse designed to ingest from all your data sources in minutes and support all your query engines at scale, for a fraction of the cost. Ingest from databases and event streams at TB-scale in near real-time, with the simplicity of fully managed pipelines. Query your data with any engine, and support all your use cases including BI, real-time analytics, and AI/ML. Cut your costs by 50% or more compared to cloud data warehouses and ETL tools with simple usage-based pricing. Deploy in minutes without engineering overhead with a fully managed, highly optimized cloud service. Unify your data in a single source of truth and eliminate the need to copy data across data warehouses and lakes. Use the right table format for the job, with omnidirectional interoperability between Apache Hudi, Apache Iceberg, and Delta Lake. Quickly configure managed pipelines for database CDC and streaming ingestion.
  • 15
    IBM watsonx.data
    Put your data to work, wherever it resides, with the open, hybrid data lakehouse for AI and analytics. Connect your data from anywhere, in any format, and access through a single point of entry with a shared metadata layer. Optimize workloads for price and performance by pairing the right workloads with the right query engine. Embed natural-language semantic search without the need for SQL, so you can unlock generative AI insights faster. Manage and prepare trusted data to improve the relevance and precision of your AI applications. Use all your data, everywhere. With the speed of a data warehouse, the flexibility of a data lake, and special features to support AI, watsonx.data can help you scale AI and analytics across your business. Choose the right engines for your workloads. Flexibly manage cost, performance, and capability with access to multiple open engines including Presto, Presto C++, Spark Milvus, and more.
  • 16
    Databricks Data Intelligence Platform
    The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.
  • 17
    Presto

    Presto

    Presto Foundation

    Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. For data engineers who struggle with managing multiple query languages and interfaces to siloed databases and storage, Presto is the fast and reliable engine that provides one simple ANSI SQL interface for all your data analytics and your open lakehouse. Different engines for different workloads means you will have to re-platform down the road. With Presto, you get 1 familar ANSI SQL language and 1 engine for your data analytics so you don't need to graduate to another lakehouse engine. Presto can be used for interactive and batch workloads, small and large amounts of data, and scales from a few to thousands of users. Presto gives you one simple ANSI SQL interface for all of your data in various siloed data systems, helping you join your data ecosystem together.
  • 18
    Apache Spark

    Apache Spark

    Apache Software Foundation

    Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.
  • 19
    Infor Data Lake
    Solving today’s enterprise and industry challenges requires big data. The ability to capture data from across your enterprise—whether generated by disparate applications, people, or IoT infrastructure–offers tremendous potential. Infor’s Data Lake tools deliver schema-on-read intelligence along with a fast, flexible data consumption framework to enable new ways of making key decisions. With leveraged access to your entire Infor ecosystem, you can start capturing and delivering big data to power your next generation analytics and machine learning strategies. Infinitely scalable, the Infor Data Lake provides a unified repository for capturing all of your enterprise data. Grow with your insights and investments, ingest more content for better informed decisions, improve your analytics profiles, and provide rich data sets to build more powerful machine learning processes.
  • 20
    Oracle Cloud Infrastructure Data Lakehouse
    A data lakehouse is a modern, open architecture that enables you to store, understand, and analyze all your data. It combines the power and richness of data warehouses with the breadth and flexibility of the most popular open source data technologies you use today. A data lakehouse can be built from the ground up on Oracle Cloud Infrastructure (OCI) to work with the latest AI frameworks and prebuilt AI services like Oracle’s language service. Data Flow is a serverless Spark service that enables our customers to focus on their Spark workloads with zero infrastructure concepts. Oracle customers want to build advanced, machine learning-based analytics over their Oracle SaaS data, or any SaaS data. Our easy- to-use data integration connectors for Oracle SaaS, make creating a lakehouse to analyze all data with your SaaS data easy and reduces time to solution.
  • 21
    e6data

    e6data

    e6data

    Limited competition due to deep barriers to entry, specialized know-how, massive capital needs, and long time-to-market. Existing platforms are indistinguishable in price, and performance reducing the incentive to switch. Migrating from one engine’s SQL dialect to another engine’s SQL involves months of effort. Truly format-neutral computing, interoperable with all major open standards. Enterprise data leaders are hit by an unprecedented explosion in computing demand for data intelligence. They are surprised to find that 10% of their heavy, compute-intensive use cases consume 80% of the cost, engineering effort and stakeholder complaints. Unfortunately, such workloads are also mission-critical and non-discretionary. e6data amplifies ROI on enterprises' existing data platforms and architecture. e6data’s truly format-neutral compute has the unique distinction of being equally efficient and performant across leading data lakehouse table formats.
  • 22
    SQream

    SQream

    SQream

    ​SQream is a GPU-accelerated data analytics platform that enables organizations to process large, complex datasets with unprecedented speed and efficiency. By leveraging NVIDIA's GPU technology, SQream executes intricate SQL queries on vast datasets rapidly, transforming hours-long processes into minutes. It offers dynamic scalability, allowing businesses to seamlessly scale their data operations in line with growth, without disrupting analytics workflows. SQream's architecture supports deployments that provide flexibility to meet diverse infrastructure needs. Designed for industries such as telecom, manufacturing, finance, advertising, and retail, SQream empowers data teams to gain deep insights, foster data democratization, and drive innovation, all while significantly reducing costs. ​
  • 23
    Dremio

    Dremio

    Dremio

    Dremio delivers lightning-fast queries and a self-service semantic layer directly on your data lake storage. No moving data to proprietary data warehouses, no cubes, no aggregation tables or extracts. Just flexibility and control for data architects, and self-service for data consumers. Dremio technologies like Data Reflections, Columnar Cloud Cache (C3) and Predictive Pipelining work alongside Apache Arrow to make queries on your data lake storage very, very fast. An abstraction layer enables IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets. Dremio’s semantic layer is an integrated, searchable catalog that indexes all of your metadata, so business users can easily make sense of your data. Virtual datasets and spaces make up the semantic layer, and are all indexed and searchable.

Data Lakehouse Guide

A data lakehouse is an analytics architecture that combines the best aspects of data lakes and data warehouses, providing a unified and secure system for storing, managing and querying all types of data. It provides an infrastructure for processing any kind of data from structured to unstructured, from streaming to batch-based in different formats. It’s the primary choice for companies who want a single source for managing big data and analytics operations.

Data lakehouses are well suited for capturing large volumes of raw data from multiple sources such as web logs, social media networks and mobile devices. This type of platform allows users to store massive amounts of structured or unstructured information without having to pay high licensing fees or hire someone with high technical skills. Data lakehouses provide an effective way to manage large datasets efficiently while maintaining security and privacy compliance standards.

They also enable faster analysis with self-service access to both structured queries (SQL) and complex machine learning algorithms (ML). With powerful automation tools, customizable pipelines, native connectors and advanced orchestration capabilities, users can quickly build robust workflows tailored precisely to their specific needs. Data lakehouses offer built-in governance features so organizations can maintain continuity through changes in personnel or ownership over time while preventing unauthorized access—allowing them full control over who has access rights and what actions they can take within the environment.

Last but not least, many data lakehouses provide analytical insights into your historical datasets by using automated machine learning capabilities such as anomaly detection into key business metrics like customer churn prediction that alert you when something out of the ordinary happens within your dataset which can help you understand end user behaviour better and make smarter decisions in real time. Data lakehouses are an ideal choice for enterprises that need to store and analyze large datasets quickly and securely.

Data Lakehouse Features

  • Storage: Data lakehouse platforms provide scalable storage for data from multiple sources. This storage can be increased or decreased depending on the platform's needs.
  • Processing: Data lakehouse platforms allow for pre-processing of data before analysis, which includes transforming and cleaning the data to make it easier for further analysis.
  • Analytics: With its analytics capabilities, a data lakehouse platform allows users to perform sophisticated analytics tasks such as predictive modeling, machine learning, and anomaly detection.
  • Visualization: The platform provides powerful visualization tools that allow users to create intuitive charts and graphs. These visuals provide insight into patterns in their data and help inform decisions.
  • Security & Governance: Security features ensure the security of sensitive information stored in a data lakehouse platform, while governance features allow organizations to configure user access levels, create roles, audit logs, and more.
  • Self-Service Reporting: Data lakehouse platforms are capable of providing self-service reports that are easy to use and interpret by business users who don’t have any technical background or knowledge.
  • Automation & Orchestration: Automation capabilities enable processes to be automated so that they run without manual input from an administrator. Orchestration capabilities allow for process orchestration so that tasks can be scheduled and executed automatically based on certain criteria or events.

Types of Data Lakehouses

  • Cloud Data Lakehouse Platforms: These are cloud-based platforms that can be deployed on multiple cloud computing services. They are designed to provide an efficient and secure way of storing, organizing, and analyzing large amounts of structured and unstructured data. These platforms typically allow users to create data warehouses with access control tools and query engines for complex analytics.
  • Shared Database Systems: These database systems allow users to share their data in a common repository. They usually allow for the creation of data models across multiple users and have built-in query capabilities, allowing different users to collaborate in accessing the same dataset.
  • On-Premise Data Lakes: These on-premise installations provide a local storage solution for big data. They can be hosted on a variety of hardware, from clusters of commodity servers up to large scale computational grids or supercomputers. Most solutions offer powerful database management tools as well as machine learning algorithms for predictive analytics.
  • Hybrid Data Lakehouses: These solutions combine elements from two or more architectures, including cloud and on-premise deployments with integrated security measures. Generally these solutions offer advanced features such as real-time streaming analytics, automated machine learning algorithms, intelligent search capability, etc., enabling organizations to gain insights from their data quickly and accurately.
  • IoT Data Lakehouse Platforms: These platforms are designed to handle high volumes of data generated from the Internet of Things (IoT) devices. They are capable of collecting and storing the data from the devices, performing real-time analytics on them, and providing actionable insights. These solutions offer scalability and agility for businesses that need to manage their IoT infrastructure efficiently.

Benefits of Data Lakehouses

  1. Cost Efficiency: Data Lakehouse platforms offer cost-effective solutions for data storage and processing. The platforms are designed to be highly scalable, allowing organizations to increase or decrease their capacity as needed without a significant investment. This helps organizations save money on hardware and software costs as well as maintenance costs associated with traditional data warehouses.
  2. Improved Speed and Performance: Data lakehouses run on modern architectures that have been carefully optimized for speed and performance. This allows organizations to process huge datasets quickly, enabling them to gain insights faster than ever before. Furthermore, the distributed nature of data lakehouses allows organizations to leverage parallel computing power, making calculations even faster.
  3. Flexibility: Data lakehouses store data in its raw form, which offers tremendous flexibility when it comes to query performance and implementation of different analytics techniques. Traditional data warehouses require users to define the schema upfront and limit their queries accordingly, whereas data lakehouses allow users to explore the entire dataset using ad hoc queries written in SQL or other programming languages such as Python or R.
  4. Comprehensive Analytics Capabilities: Data lakehouses offer comprehensive analytics capabilities that go beyond traditional relational databases, including support for advanced analytics functions such as machine learning (ML) and artificial intelligence (AI). Organizations can use these features to unlock valuable insights from their datasets that would otherwise remain hidden within their structured formats.
  5. Security: Data Lakehouse platforms provide robust security features like encryption at rest, secure access control mechanisms, real-time monitoring of user activity, and multiple layers of protection against cyberattacks. The increased security measures help ensure maximum privacy and protection when handling sensitive customer information or proprietary company assets in the cloud environment.

Data Lakehouse Trends

  1. Increased Popularity: Data lakehouse platforms have become increasingly popular in recent years, as businesses recognize the potential of managing their data in one central platform. This has resulted in an increase in investments and development efforts to improve the capabilities and features of these platforms.
  2. Automation and Machine Learning: Many data lakehouse platforms now feature automation and machine learning capabilities that make it easier for businesses to organize, analyze, and extract insights from their data. This allows them to gain deeper insights into customer behavior, market trends, and other business-critical metrics.
  3. Improved Security: Data lakehouse platforms typically offer improved security features compared to traditional data warehouses. These features include encryption, access control, and granular authorization for users. This helps businesses ensure that their data is secure and only accessible by authorized personnel.
  4. Integration with Other Platforms: Data lakehouse platforms are designed to integrate with other systems such as databases, analytics tools, and cloud services. This makes it easier for businesses to access their data from any location and share their insights with other teams or departments within the organization.
  5. Cost Savings: Data lakehouse platforms are typically more cost-effective than traditional systems due to their scalability and ability to run on commodity hardware. This allows businesses to save money on hardware costs while still being able to manage large amounts of data efficiently.

How to Choose the Right Data Lakehouse

  1. Determine Your Needs: The first step to selecting the right data lakehouse platform is to assess and define your needs. Ask yourself questions such as how much storage you need, how much computing power you need, what security protocols are necessary, do you need a self-service or managed service, etc.
  2. Research Platforms: Once you have determined your needs, research the different existing platforms that could meet them. Compare features like cost, scalability, open source options, ease of use, documentations and support services. Compare data lakehouse solutions using the comparison tools on this page and filter by user reviews, features, integrations, operating system, pricing, and more.
  3. Review User Reviews: Look at user reviews for the platforms that meet your needs to get an understanding of how others feel about them. These reviews can provide details about features that may not be readily available from the company's website or other sources.
  4. Test & Evaluate: After narrowing down your choices to a few options that meet all of your criteria and have positive reviews from users, it's time to test out each one for yourself by setting up a proof-of-concept environment and evaluating their performance in real scenarios with actual data sets to ensure they produce desired results.
  5. Make Your Choice: Finally once you have tested out each potential data lakehouse platform’s ability to suit your business goals make sure the team understands its capabilities and any limitations it may have before making the final selection and committing long term resources on its implementation and usage.

Who Uses Data Lakehouses?

  • Business Users: These are people who interact with, analyze and use the data lakehouse platform to gain insights into their business operations. They often work in marketing, finance, operations, or sales departments and require the ability to access a wide range of data sources and formats quickly.
  • Data Scientists: These users leverage machine learning algorithms to process raw data for predictive analytics purposes. They may use the platform to develop models, run experiments, and generate reports that enable them to better understand trends in customer behavior.
  • Data Engineers: This type of user builds and maintains pipelines between data sources and storage areas within the platform. They also ensure that the structure of incoming data is consistent across different sources for optimal performance when running queries or generating reports.
  • AI/ML Practitioners: These users use deep learning techniques or other artificial intelligence methods on large datasets stored in a lakehouse platform to detect patterns, classify objects, make predictions, or create clusters of related items.
  • Analysts: These users explore unstructured or semi-structured datasets stored in a lakehouse platform by running structured queries against them in order to uncover deeper insights into customer behavior or other phenomenon related to their job scope.
  • Administrators: Administrators manage all aspects of a lakehouse platform such as user accounts, quotas, permissions settings and security policies. They have an understanding of how technical components interact with each other ensuring high levels of functionality for all users within an organization’s system.

How Much Does a Data Lakehouse Cost?

The cost of data lakehouse platforms can vary significantly depending on the number of users, complexity of project, and other factors. Generally speaking, data lakehouse platforms range from a few hundred dollars to several thousand dollars depending on the features and services desired.

For businesses looking for a basic version with limited or no integration capabilities, some data lakehouse providers offer plans starting at around $200/month. These plans often include storage space, as well as access to essential analytics tools and user-friendly interfaces.

For more advanced users who need more sophisticated analysis capabilities, such as predictive modeling or machine learning algorithms, these plans can quickly increase up to $1,000/month or more. This includes features like automated feature engineering and model selection tools that require significant computing power and cloud storage capacity. Additionally these plans may also come with additional support services such as data transformation services or specialized consultancy services tailored specifically to the customer's needs.

Finally, enterprise-level customers may find themselves paying thousands of dollars each month for access to a full suite of analytic technology including an in-depth data exploration process with powerful visualization capabilities along with continuous analytics & ML monitoring capabilities across all data sources involved in the project.

Overall pricing for data lakehouse platforms can be quite complex due to their custom nature so it's important for organizations seeking this type of service to do thorough research before committing to any particular provider or plan.

What Integrates With Data Lakehouses?

Data lakehouse platforms can integrate with a variety of types of software, including data visualization and analytics tools, streaming architectures, machine learning frameworks, workflow applications, ETL/ELT pipelines, and cloud services. Data visualization tools allow users to quickly and easily explore their data by creating visualizations such as charts and graphs. Analytics tools provide more sophisticated analysis capabilities for deeper insights into the data. Streaming architectures enable real-time processing of large volumes of incoming data streams. Machine learning frameworks allow for the development and deployment of predictive models. Workflow applications enable complex workflows to be automated so that tasks can be completed quickly and efficiently. ETL/ELT pipelines facilitate the transfer of data from one system to another. Finally, cloud services can be used to store data on public or private clouds in order to improve scalability and availability.