Data Lakehouse Companies and Their Solutions for Cloud Data

Data lakehouse companies are revolutionizing the way we store and manage data in the cloud. They're making it possible to have both the scalability of a data lake and the governance of a data warehouse in one place.

Companies like Databricks, Snowflake, and Amazon Redshift are leading the charge in this space. Databricks, for example, offers a cloud-based data lakehouse platform that's optimized for big data and analytics workloads.

Data lakehouse companies are solving the problem of data fragmentation by providing a single platform for storing and managing data from multiple sources. This makes it easier to get insights from your data and make better business decisions.

Their solutions are designed to be highly scalable and flexible, allowing you to handle large volumes of data and perform complex analytics.

A different take: Cloud Data Store

A data lakehouse is a hybrid architecture that combines the best features of data lakes and data warehouses. It allows for structured data management alongside the ability to handle unstructured data.

This means that a data lakehouse can store and manage both raw, unstructured data and structured data, giving organizations the flexibility they need to analyze and understand their data.

One of the key benefits of a data lakehouse is that it offers advanced analytics and query performance, making it an attractive option for organizations that need to extract insights from their data.

By combining the strengths of data lakes and data warehouses, a data lakehouse provides a single platform for data management, reducing the complexity and cost associated with managing multiple systems.

Here are the key features of a data lakehouse:

A data lakehouse offers a range of benefits, including the ability to store vast amounts of raw, unstructured data in a cost-effective manner.

Data lakes empower business users to find relevant information faster, regardless of sources or locations, enabling more effective analyses, deeper cross-organization collaboration, and faster, more informed decision-making.

A data lakehouse provides a common repository to collect and store data, streamlined usage from a single source, and access to the raw data necessary for today’s advanced analytics and artificial intelligence (AI) needs.

Here are some key features of a data lake:

Additionally, a data lakehouse provides features like fully managed table format, Apache Iceberg table format, polyglot, multi-cluster compute engine, and cost-effective performance for high concurrency.

A data lake offers a lot of flexibility, and one of its key features is the separation of storage and compute. This means you can store your data in a central location and process it in a separate environment.

Here are some key features of a data lake:

In addition to these features, a data lakehouse offers some extra benefits, including fully managed table formats and a polyglot, multi-cluster compute engine.

A data lakehouse pattern provides the following features:

These features make it easier to work with your data and get the insights you need.

Vertica Unified Analytics offers a columnar storage architecture that allows for efficient querying on large datasets.

This architecture is particularly useful for high-performance analytics, making it a great choice for handling big data.

Vertica integrates with a wide range of data sources, giving you the flexibility to work with various types of data.

It supports advanced analytics including machine learning and geospatial analytics, opening up new possibilities for data-driven insights.

Vertica also offers a hybrid deployment model, supporting on-premises, cloud, and multi-cloud environments.

You can choose to manage the platform yourself or run Vertica-as-a-service, providing flexibility in how you deploy and utilize the platform.

Discover more: Data Lake Analytics

Data lakehouse companies are revolutionizing the way we store, manage, and analyze data. Google Cloud Platform offers two options for building data lakes: Google Cloud Storage (GCS) and BigLake, which can span across warehouses, object stores, and clouds.

BigLake is particularly useful for managing distributed data, allowing you to unify data warehouses and lakes into a consistent format for faster data analytics. It supports Amazon S3 and Azure data lake storage Gen 2, eliminating the need to duplicate or move data.

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Snowflake Data Cloud is a flexible platform that can be used for various workloads, including collaboration, analytics, and AI and ML. It's a single platform that can power multiple types of workloads, making it a great option for organizations with diverse data needs.

Here are some key features of data lakehouse companies:

Starburst Data Lakehouse offers flexibility and live, interactive querying capabilities, making it a great option for analytics use cases. It supports over 50 data sources, including legacy enterprise sources, and can connect with various data formats and engines.

Google Cloud Platform offers a couple options for building data lakes, including Google Cloud Storage (GCS) and the new BigLake solution.

Google Cloud Storage is an object storage service that's a great fit for organizations that plan to stay within Google's cloud ecosystem. BigLake, on the other hand, is a storage engine that extends storage capabilities to open formats on multi-cloud object stores.

For more insights, see: Google Data Lake

BigLake supports Amazon S3 and Azure data lake storage Gen 2, eliminating the need to duplicate or move data and reducing cost and inefficiencies. This means you can choose the best analytics tools regardless of where or how data is stored.

The added structure and governance from Dataplex makes BigLake an intriguing data lakehouse option. Sudhir Hasbe, Google Cloud's senior director of Product Management for data analytics, discussed BigLake in 2022, saying it will seamlessly integrate with Dataplex for consistent data management and governance.

BigLake enables organizations to unify data warehouses and lakes into a consistent format for faster data analytics. This is made possible by its ability to manage data as if it were BigQuery tables.

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Azure Storage is a robust platform that integrates seamlessly with Azure services, making it a compelling choice for businesses already invested in the Azure ecosystem. Azure Data Lake Storage, in particular, offers a set of capabilities that can be used with the Blob Storage service of your Azure Storage account.

ADLS focuses on enterprise-grade security, data governance, and compliance features, providing built-in data encryption and granular access control policies to meet stringent security and compliance requirements. This is especially beneficial for organizations with strict data protection needs.

Azure Private Link enables secure and private access to data lakes over a private network connection, which is a significant advantage for organizations with high security demands. This feature is particularly useful for businesses that require extra protection for their sensitive data.

However, ADLS's pay-as-you-go pricing model can lead to unexpected costs if not managed effectively, which is a common challenge faced by organizations using similar data lake vendors.

Broaden your view: Why Is Data Protection Important

Cloud data lakehouse companies are offering a range of solutions to help organizations manage and analyze their data. Google Cloud Platform, for instance, offers Google Cloud Storage and BigLake, which can be used to build a distributed data lake that spans across warehouses, object stores, and clouds.

BigLake is particularly interesting because it allows you to manage your data as if it were BigQuery tables and simplifies access control management. It also supports Amazon S3 and Azure data lake storage Gen 2, eliminating the need to duplicate or move data.

Snowflake is another cloud data lakehouse company that offers a flexible platform for various workloads, including collaboration, analytics, data exploration, and AI/ML. It allows you to use traditional business intelligence tools and newer technologies like data science and applications.

Snowflake's capabilities include data engineering, data lakes, collaboration, AI and data science, data warehousing, marketing, and cybersecurity. You can sign up for a free trial to see its capabilities for yourself.

Databricks is another cloud data lakehouse company that offers a lakehouse built on Databricks, which replaces the current dependency on data lakes and data warehouses for modern data companies. Some key tasks you can perform on Databricks include real-time data processing, data integration, schema evolution, data transformations, and data analysis and reporting.

Here are some key benefits of cloud data lakehouse companies:

These cloud data lakehouse companies are changing the way organizations manage and analyze their data, offering flexibility, scalability, and cost-effectiveness.

Data governance is critical for the successful management of data lakes and lakehouses. It should address key elements such as data quality, security, compliance, and usage policies.

A strong data governance framework ensures that data remains reliable and trustworthy by establishing clear roles and responsibilities for data stewardship and robust auditing processes. This framework also empowers users to access and utilize data responsibly.

Organizations can prevent data swamps and maximize the value derived from their data lakes by prioritizing user access controls, implementing robust data governance policies, and utilizing automated data quality checks. This enables data discovery for analysts and protects sensitive information.

Implementing a dedicated metadata management tool, such as a data catalog, allows organizations to collect and utilize metadata, improving data discoverability and enhancing data quality. This is essential for transforming a data lake into a trusted and reliable resource.

Here's an interesting read: What Is Data Quality and Why Is It Important

Data governance is the backbone of a healthy data lake, and it's essential to prioritize it to avoid data swamps. A strong data governance framework addresses key elements such as data quality, security, compliance, and usage policies.

Implementing a data governance framework is critical for the successful management of data lakes and lakehouses. This framework should establish clear roles and responsibilities for data stewardship, along with robust auditing processes.

Effective governance not only mitigates risks associated with data management but also empowers users to access and utilize data responsibly. By prioritizing data governance, organizations can harness the full potential of their data assets while maintaining compliance and safeguarding sensitive information.

A lack of data governance can lead to data that's misused, held too long, or otherwise corrupts data-driven processes. Poor or nonexistent data governance is a common sign of a data swamp.

Data governance helps keep data quality high and data literacy efforts on track. Establishing clear roles and responsibilities for data stewardship is a crucial aspect of data governance.

Regularly assessing the health of a data lake is essential for ensuring it remains an effective resource. Key performance indicators (KPIs) such as data quality metrics, retrieval speeds, and user engagement levels can provide valuable insights into the lake's performance.

A dedicated metadata management tool, such as a data catalog, allows organizations to collect and utilize metadata, providing context to the raw data stored. This enables easy storage and searching of metadata.

By implementing strong metadata management practices, organizations can improve data discoverability, enhance data quality, and ensure compliance with regulations.

Democratizing access to data is crucial for any organization. Requests for data sets can be a major bottleneck, taking hours or even days to fulfill.

This was the case for Banco Inter, where data sets took a significant amount of time to access. Time-to-insight was reduced to seconds with the right solution.

Using a tool like Starburst can make a huge difference. It allowed Banco Inter to explore near real-time data on and around their data lake.

Data ingestion is the first step in building a data lakehouse, and it's crucial to get it right. At the ingestion layer, batch or streaming data arrives from a variety of sources and in a variety of formats.

You can use Delta Lake to convert those files to Delta tables and enforce schema. This helps catch missing or unexpected data before it causes problems downstream.

Unity Catalog allows you to register tables according to your data governance model and required data isolation boundaries.

For more insights, see: Azure Data Ingestion

Amazon S3 is a popular storage platform for building and storing data lakes due to its high availability and low latency access.

It's especially attractive for organizations that want to leverage other AWS services like Aurora.

Amazon S3 has seamless integration with the AWS ecosystem, including AWS Glue for data cataloging and Amazon Athena for ad-hoc querying.

However, S3 will lack some metadata structure required for advanced data management tasks if not paired with Glue or another metastore/catalog solution.

This can be overwhelming for non-experts, requiring specialized expertise to navigate effectively.

Discover more: Aws Data Lakehouse

Ingestion is the first step in the data processing pipeline, where data arrives from various sources in different formats.

Data can arrive in batch or streaming formats, and it's essential to have a place for it to land in its raw format.

You can use the schema enforcement capabilities of Delta Lake to check for missing or unexpected data as you convert those files to Delta tables.

A unique perspective: Data Lake vs Delta Lake vs Lakehouse

Unity Catalog allows you to register tables according to your data governance model and required data isolation boundaries.

This helps track the lineage of your data as it's transformed and refined, keeping sensitive data private and secure.

By registering tables in Unity Catalog, you can apply a unified governance model to ensure data consistency and integrity.

Check this out: Data Lake Catalog

Requests for data sets used to take hours or even days to fulfill, requiring a lot of movement between zones in the data lake.

Time-to-insight was significantly reduced to just seconds by using Starburst to explore near real-time data on and around the data lake.

A unified governance model allows you to track data lineage back to your single source of truth.

Data layouts, optimized for different tasks, enable end users to access data for machine learning applications, data engineering, and business intelligence and reporting.

The final layer of the data serving process serves clean, enriched data to end users, designed to serve data for all your use cases.

Data lakehouse companies face several challenges and limitations. One major limitation is the complexity of integrating data from various sources, as seen in the example of Databricks, which requires significant engineering effort to set up.

The sheer volume of data in a data lakehouse can be overwhelming, making it difficult to manage and govern. In the case of Google Cloud Bigtable, it's designed to handle large amounts of data, but still requires careful planning to ensure data quality and integrity.

Data security and compliance are also significant concerns, particularly in industries with strict regulations, such as finance and healthcare. For instance, Snowflake's data lakehouse is built with security and compliance in mind, but still requires careful configuration to meet specific requirements.

Data lakehouse companies must also balance the need for scalability with the need for performance, which can be a challenge when dealing with large datasets. Databricks' architecture is designed to scale, but still requires optimization to achieve optimal performance.

Consider reading: Azure Data Security

Companies like Grubhub, Verizon, and Lucid have chosen Starburst to break down data silos and increase time-to-insight.

These companies are among the most data-driven on the planet, relying on data to inform their business decisions.

Genus, a company that has also implemented Starburst, has a Senior Manager of Data Science & Data Engineering who can attest to the benefits of a data lakehouse solution.

Hundreds of companies have successfully implemented Starburst, including those in the data-driven industry.

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Data lakehouses are a fusion of data warehouses and data lakes, offering a single repository for both raw and processed data.

This architectural approach combines the benefits of data warehouses, such as structured data and analytics, with the flexibility of data lakes, which support unstructured data and machine learning.

Data lakehouses are designed to handle large volumes of data from various sources, including social media, IoT devices, and applications.