What is Data Lakehouse and other frequently asked questions (2023)

problem index

What is a Data Lake House?
What is a data lake?
What is a data warehouse?
How is a Data Lakehouse different from a Data Warehouse?
How is a Data Lakehouse different from a data lake?
How easy is it for data analysts to use Data Lakehouse?
How does a Data Lakehouse system compare to a data warehouse in terms of performance and cost?
What data governance functions does the Data Lakehouse system support?
Must the Data Lakehouse be centralized or can it be decentralized into a data grid?
How is Data Mesh related to Data Lakehouse?

What is a Data Lake House?

In short, Data Lakehouse is an architecture that enables efficient and secure artificial intelligence (AI) and business intelligence (BI) directly on the massive amounts of data stored in the data lake.

Discover why lakehouses are the data architecture of the futureWith Bill Inmon, the father of data warehousing.

Today, the vast majority of enterprise data resides indata lake, a low-cost storage system that can manage any type of data (structured or unstructured), and has an open interface that any processing tool can run on. These data lakes are where most data transformation and advanced analytics workloads (such as AI) run to leverage the full set of data in an organization. Also, for business intelligence (BI) use cases, proprietary data warehouse systems are used to structure a small subset of data. These data warehouses primarily support BI, using SQL to answer historical analysis questions about the past (for example, what was my income last quarter), while data lakes store large amounts of data and support analysis using SQL and non-SQL interfaces, including predictive analysis and AI (e.g. which of my customers are likely to churn, or what coupons to offer my customers at what time). Historically, to achieve both AI and BI, you had to have multiple copies of data and move it between data lakes and data warehouses.

(Video) What is a Data Lakehouse? A Simple Explanation for Anyone

data lake houseYou can store all your data at once in a data lake and do AI and BI directly on that data. It has specific capabilities to efficiently enable AI and BI on large-scale data across all enterprises. Namely, it has the SQL and performance features (indexing, caching, MPP processing) that make BI run fast on the data lake. It also features direct file access and direct native support for Python, data science, and AI frameworks without being enforced through SQL-based data warehouses. The key technologies used to implement Data Lakehouses are open source, such asdelta lake, Woody And Iceberg. Vendors focused on Data Lakehouse include, but are not limited to, Databricks, AWS, Dremio, and Starburst. Vendors that provide data warehouses include, but are not limited to, Teradata, Snowflake, and Oracle.

More recently, Bill Inmon, widely considered the father of the data warehouse,The Evolution of Data LakehousesExplains lakehouse's unique ability to manage data in an open environment while combining the data science focus of a data lake with the end-user analytics of a data warehouse.

What is a data lake?

A data lake is a low-cost, open, durable storage system for any data type—tabular data, text, images, audio, video, JSON, and CSV. In the cloud, every major cloud provider leverages and promotes data lakes such as AWS S3, Azure Data Lake Storage (ADLS), Google Cloud Storage (GCS). As a result, the vast majority of data for most organizations is stored in cloud data lakes. Over time, most organizations store their data in an open standardized format, usually Apache Parquet format or ORC format. As a result, a large ecosystem of tools and applications can directly consume these open data formats. This approach to storing data in an open format at a very low cost enables organizations to amass large amounts of data in data lakes while avoiding vendor lock-in. At the same time, despite these advantages, the data lake still suffers from three main problems - security, quality and performance. Since all data is stored and managed in the form of files, it does not provide fine-grained access control to file content, but only coarse-grained access control, that is, who can access which files or directories. Query performance is poor because the format is not optimized for fast access and listing files is computationally expensive. In short, organizations end up moving data to other systems to take advantage of it unless the application can tolerate noise (i.e. machine learning). Finally, quality is a challenge because it is difficult to prevent data corruption and manage schema changes as more data is pulled into the data lake. Likewise, ensuring atomic operations when writing to a set of files is challenging, and there is no mechanism for rolling back changes. As a result, many believe that most data lakes end up as data "swamps." .So most organizations move a subset of this data into a data warehouse, which doesn't have these three problems, but suffers from others.

What is a data warehouse?

A data warehouse is a proprietary system dedicated to storing and managing structured or semi-structured (mostly in JSON format) data for SQL-based analytics and business intelligence. The most valuable business data is collated and uploaded to data warehouses that are optimized for high performance, concurrency, and reliability, but at a much higher cost because any data processing has to happen at the more expensive SQL rate , not cheap data lake access rates. Historically, data warehouses have been limited in capacity to support both ETL and BI queries; let alone live streaming. Since the data warehouse is mainly built for structured data and does not support unstructured data such as images, sensor data, documents, and videos, it has limited support for machine learning and cannot directly support popular open source libraries and tools (TensorFlow, PyTorch). and other Python-based libraries). As a result, most organizations end up keeping these datasets in data lakes, moving subsets into data warehouses for fast concurrent BI and SQL use cases

(Video) Database vs Data Warehouse vs Data Lake | What is the Difference?

How Data Lakehouse is Different from Data Warehouse

Lakehouse builds on existing data lakes, which typically contain more than 90% of the data in an enterprise. While most data warehouses support "external table" functionality to access that data, they have severe functional limitations (eg, only read operations are supported) and performance limitations in doing so. Lakehouse instead adds traditional data warehouse capabilities to an existing data lake, includingacid business, fine-grained data security, low-cost updates and deletes, first-class SQL support, optimized SQL query performance, and BI-style reporting. Lakehouse is built on the data lake to store and manage all existing data in the data lake, including various data such as text, audio and video, and structured data in tables. Unlike data warehouses, Lakehouse also natively supports data science and machine learning use cases by providing direct access to data using open APIs and supporting various ML and Python/R libraries such as PyTorch, Tensorflow or XGBoost. Therefore, Lakehouse provides a single system to manage all the data of the enterprise while supporting the analytical scope of BI and AI.

Data warehouses, on the other hand, are proprietary data systems built specifically for SQL-based structured data and certain types of semi-structured data analysis. Data warehouses have limited support for machine learning and cannot support running popular open source tools natively without first exporting the data (via ODBC/JDBC or data lakes). Today, no data warehouse system can natively support all the existing audio, image, and video data already stored in the data lake.

How is a Data Lakehouse different from a data lake?

The most common complaint about data lakes is that they can become data swamps. Anyone can dump any data into the data lake; data in the lake has no structure or governance. Performance is poor because data is not organized with performance in mind, resulting in limited analytics on the data lake. As a result, since data lakes use underlying low-cost object storage, most organizations use data lakes as a landing zone for the majority of their data, which is then moved to different downstream systems (such as data warehouses) to extract value.

Lakehouse solves the fundamental problem of getting data to emerge from a data lake. It adds ACID transactions to ensure consistency when multiple parties read or write data at the same time. It supports DW schema architectures such as star/snowflake schema and provides powerful governance and auditing mechanisms directly on the data lake. It also utilizes various performance optimization techniques such as caching, multidimensional clustering, and data skipping, using file statistics and data compression to resize files for fast analysis. It also adds fine-grained security and auditing capabilities to data governance. By adding data management and performance optimization to an open data lake, Lakehouse natively supports BI and ML applications.

(Video) Understanding Data Lakehouse

How easy is it for data analysts to use Data Lakehouse?

Data Lakehouse systems implement the same SQL interfaces as traditional data warehouses, so analysts can connect to them in existing BI and SQL tools without changing their workflow. For example, leading BI products like Tableau, PowerBI, Qlik, and Looker can all connect to Data Lakehouse systems, data engineering tools like Fivetran and dbt can run against them, and analysts can export data to desktop tools like Microsoft Excel. Lakehouse's support for ANSI SQL, fine-grained access control, and ACID transactions enables administrators to manage them in the same way as data warehouse systems, but in one system covering all the data in their organization.

An important advantage of Lakehouse systems in terms of simplicity is that they manage all data in an organization, so data analysts can be granted access to process raw and historical data as it arrives, not just loaded into the A subset of data in a warehouse system. As a result, analysts can easily ask questions that span multiple historical datasets, or build a new pipeline to process new datasets, without preventing DBAs or data engineers from loading the appropriate data. Built-in support for AI also makes it easy for analysts to run AI models built by the machine learning team on any data.

How does a Data Lakehouse system compare to a data warehouse in terms of performance and cost?

Data Lakehouse systems are built around independent, elastically scalable compute and storage to minimize their operating costs and maximize performance. Recent systems use the same optimization techniques inside their engines (for example, query compilation and storage layout optimizations) to deliver comparable or even better performance for SQL workloads than traditional data warehouses. In addition, Lakehouse systems often take advantage of cloud provider cost-saving features such as spot instance pricing (which requires the system to tolerate losing worker nodes during queries) and reduced prices for infrequently accessed storage that traditional data warehouse engines typically do not have Features are designed to support.

What data governance functions does the Data Lakehouse system support?

By adding a management interface on top of data lake storage, the Lakehouse system provides a unified way to manage access control, data quality, and compliance for all of an organization's data, using a standard interface similar to those found in data warehouses. Modern Lakehouse systems support fine-grained (row, column, and view level) access control through SQL, query auditing, attribute-based access control, data versioning, and data quality constraints and monitoring. These capabilities are typically provided using standard interfaces familiar to database administrators (for example, the SQL GRANT command) to allow existing personnel to manage all data in an organization in a unified manner. Centralizing all data in a Lakehouse system using a single management interface also reduces the administrative burden and potential for errors that come with managing multiple independent systems.

(Video) KNOW the difference between Data Base // Data Warehouse // Data Lake (Easy Explanation👌)

Must the Data Lakehouse be centralized or can it be decentralized into a data grid?

No, organizations do not need to centralize all data in one Lakehouse. Many organizations using the Lakehouse architecture take a decentralized approach to storing and processing data, but a centralized approach to security, governance, and discovery. Depending on organizational structure and business needs, we see some common approaches:

  • Each business unit builds its own Lakehouse to gain a complete view of its business—from product development to customer acquisition to customer service.
  • Each functional area, such as product manufacturing, supply chain, sales and marketing, can build its own Lakehouse to optimize operations within its business domain.
  • Some organizations also launch a new Lakehouse to address new cross-functional strategic initiatives, such as customer 360 or unexpected crises like the COVID pandemic, to drive fast, decisive action.

The unified nature of the Lakehouse architecture enables data architects to build simpler data architectures to match business needs without complexorchestrationData movement across siled data stacks for BI and ML. Additionally, the openness of Lakehouse's architecture enables organizations to leverage a growing ecosystem of open technologies without fear of being locked in to meet the unique needs of different business units or functional areas. Because lakehouse systems are typically built on independent, scalable cloud storage, it's also simple and efficient to give multiple teams access to each lakehouse. recent, delta shareWith the support of many different vendors, an open and standard mechanism for sharing data across Lakehouses is proposed.

How is Data Mesh related to Data Lakehouse?

Zhamak Dehghani outlines four basic organizing principles that embody any data grid implementation. Data Lakehouse architecture can be used to implement these organizing principles:

    • Domain-Oriented Decentralized Data Ownership and Architecture: As discussed in the previous section, the Lakehouse architecture takes a decentralized approach to data ownership. Organizations can create many different lakehouses to meet the individual needs of business groups. Depending on their needs, they can store and manage a variety of data—images, video, text, structured tabular data, and related data assets such as machine learning models and associated code to reproduce conversions and insights.
    • Data as product:The Lakehouse architecture helps organizations manage data as a product by providing complete control over the data lifecycle to different data team members within domain-specific teams. Data teams consisting of data owners, data engineers, analysts, and data scientists can manage data (structured, semi-structured, and unstructured data with appropriate lineage and security controls), code (ETL, data science notebooks, ML training and deployment) and supporting infrastructure (storage, compute, cluster strategies, and various analytics and ML engines). Lakehouse platform features such as ACID transactions, data versioning, and zero-copy cloning make it easy for these teams to release and maintain their data as production.
    • Self-service data infrastructure as a platform: The Lakehouse architecture provides an end-to-end data platform for data management, data engineering, analytics, data science, and machine learning, and integrates with a broad ecosystem of tools. Adding data management on top of an existing data lake simplifies data access and sharing—anyone can request access, the requester pays for cheap blob storage and gets secure access instantly. Additionally, using open data formats and enabling direct file access, data teams can use best-in-class analytics and ML frameworks on their data.
    • Federated Computing Governance:Governance in the Lakehouse architecture is achieved through a centralized catalog with fine-grained access control (row/column level), enabling easy discovery of data and other artifacts such as code and ML models. Organizations can assign different administrators to different parts of the directory to decentralize control and management of data assets. This hybrid approach of centralized catalog and federated control preserves the independence and agility of local domain-specific teams, while ensuring reuse of data assets across those teams and enforcing a common security and governance model globally.


What is Data Lakehouse and other frequently asked questions? ›

In short, a Data Lakehouse is an architecture that enables efficient and secure Artificial Intelligence (AI) and Business Intelligence (BI) directly on vast amounts of data stored in Data Lakes. Explore why lakehouses are the data architecture of the future with the father of the data warehouse, Bill Inmon.

What is the definition of data Lakehouse? ›

A data lakehouse is a hybrid data management architecture that combines the flexibility and scalability benefits of a data lake with the data structures and data management features of a data warehouse.

What is the difference between data Lakehouse and data hub? ›

In summary, a data hub is about sharing and exchanging curated and managed data between systems, services, or parties. A data lake is about creating a vast pool of data in many different formats which can feed analytics, AI or data science services to create value.

What are the different types of data lakes? ›

A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and binary data (images, audio, video).

What are the key features of data lakehouse? ›

Data Lake Features
  • Separation of storage and compute.
  • Unlimited scale data repository.
  • Mixed data types: structured, semi-structured and unstructured.
  • Choice of languages for processing (but not always SQL)
  • No need to inventory or ingest data.
  • Direct access to source data.

What is an example of a data lakehouse? ›

Some examples of data lakehouses include Amazon Redshift Spectrum or Delta Lake.

What is the advantage of data Lakehouse? ›

A data lakehouse offers more robust data governance than a traditional data warehouse. This is because it enforces strict controls on who can access and modify data. This helps to ensure that only authorized users can access sensitive information.

Does a data lakehouse replace a data warehouse? ›

A data lake is not a direct replacement for a data warehouse; they are supplemental technologies that serve different use cases with some overlap. Most organizations that have a data lake will also have a data warehouse.

What is the difference between Lakehouse and data lake? ›

The data lakehouse has a layer design, with a warehouse layer on top of a data lake. This architecture, which enables combining structured and unstructured data, makes it efficient for business intelligence and business analysis. A data lakehouse system usually consists of the following layers: Ingestion.

What is the difference between data Lakehouse and Data Mart? ›

A data mart is a data warehouse that serves the needs of a specific business unit, like a company's finance, marketing, or sales department. On the other hand, a data lake is a central repository for raw data and unstructured data. You can store data first and process it later on.

Is SQL a data lake? ›

SQL is the solution. A data lake is a centralized repository that allows for the storage of structured and unstructured data at any scale. SQL (Structured Query Language) is a programming language used to communicate with and manipulate databases.

Is Snowflake a data lake or warehouse? ›

Snowflake Has Always Been a Hybrid of Data Warehouse and Data Lake.

How many layers are in data lake? ›

If you work with non-sensitive data, such as non-personally identifiable information (PII) data, we recommend that you use at least three different data layers in a data lake on the AWS Cloud. However, you might require additional layers depending on the data's complexity and use cases.

How is data stored in a data lakehouse? ›

In a two-tier data architecture, data is ETLd from the operational databases into a data lake. This lake stores the data from the entire enterprise in low-cost object storage and is stored in a format compatible with common machine learning tools but is often not organized and maintained well.

How do you implement a data lakehouse? ›

5 Steps to a Successful Data Lakehouse
  1. Start with the data lake that already manages most of the enterprise data.
  2. Bring quality and governance to your data lake.
  3. Optimize data for fast query performance.
  4. Provide native support for machine learning.
  5. Prevent lock-in by using open data formats and APIs.

What are the applications of data Lakehouse? ›

With a data lakehouse, an array of different tools and engines can access the raw data in an object store directly via the DataFrames application programming interface (API). This enables instant data optimization and presentation based on the needs of a certain workload, say machine learning.

Which of the following is a common problem with data lakes? ›

Lack of security features

Data lakes are hard to properly secure and govern due to the lack of visibility and ability to delete or update data. These limitations make it very difficult to meet the requirements of regulatory bodies.

What data is stored in data lake? ›

A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits. Learn more about modernizing your data lake on Google Cloud.

What are the limitations of data lake? ›

Having a large number of small files in a data lake (rather than larger files optimized for analytics) can slow down performance considerably due to limitations with I/O throughput.

Which two services are used for persistence of the data in Lakehouse? ›

OCI Object Storage and OCI Data Catalog, services are used for persistence of the data in Lakehouse.

What is the difference between database and data lake? ›

What is the difference between a database and a data lake? A database stores the current data required to power an application. A data lake stores current and historical data for one or more systems in its raw form for the purpose of analyzing the data.

What is the difference between ETL and data lake? ›

Data Lake defines the schema after data is stored, whereas Data Warehouse defines the schema before data is stored. Data Lake uses the ELT(Extract Load Transform) process, while the Data Warehouse uses ETL(Extract Transform Load) process.

Is data Lakehouse open-source? ›

An open data lakehouse is a powerful and cost-effective solution for managing and analyzing open data. Leveraging open-source technologies to provide flexibility and transparency enables efficient data analysis, eliminates data silos, and enables data-driven decisions.

How is Lakehouse different from warehouse? ›

Itcan store both structured and unstructured data, whereas structure is required for a warehouse. The data warehouse is tightly coupled, whereas Lakes have decoupled compute and storage. Lakes are easy to change and scale in comparison with a warehouse. Data retention in the warehouse is less due to storage expense.

What are the three types of data mart? ›

Three basic types of data marts are dependent, independent, and hybrid.

What is the difference between big data and data lake? ›

Hosting, Processing and Analyzing structured, semi and unstructured in batch or real-time using HDFS, Object Storage and NoSQL databases is Big Data. Whereas Hosting, Processing and Analyzing structured, semi and unstructured in batch or real-time using HDFS and Object Storage is Data Lake.

Is S3 a data lake? ›

Amazon S3 provides an optimal foundation for a data lake because of its virtually unlimited scalability and high durability. You can seamlessly and non-disruptively increase storage from gigabytes to petabytes of content, paying only for what you use.

Is data lake an ETL tool? ›

Data Lakes and ETL

In more simple terms, data warehouses are systems which contain current and historical data that has been processed and standardized. The warehouse is the central location from which all data is retrieved. In contrast, data lakes are repositories of data in a more fluid sense (pun inevitable).

Do data lakes use ETL? ›

ETL is what happens within a Data Warehouse and ELT within a Data Lake. ETL is the most common method used when transferring data from a source system to a Data Warehouse.

Is Kafka part of the data lake? ›

A modern data lake solution that uses Apache Kafka, or a fully managed Apache Kafka service like Confluent Cloud, allows organizations to use the wealth of existing data in their on-premises data lake while moving that data to the cloud.

Is Hadoop a data lake? ›

Hadoop is an important element of the architecture that is used to build data lakes. A Hadoop data lake is one which has been built on a platform made up of Hadoop clusters. Hadoop is particularly popular in data lake architecture as it is open source (as part of the Apache Software Foundation project).

What is ETL in Snowflake? ›

ETL, which stands for “extract, transform, load,” are the three processes that, in combination, move data from one database, multiple databases, or other sources to a unified repository—typically a data warehouse.

Is Snowflake a database or ETL? ›

Snowflake supports both ETL and ELT and works with a wide range of data integration tools, including Informatica, Talend, Tableau, Matillion and others.

What are the zones in a data lake? ›

No two data lakes are built exactly alike. However, there are some key zones through which the general data flows: the ingestion zone, landing zone, processing zone, refined data zone and consumption zone.

What format is data lake data? ›

A data lake is a storage repository that holds a large amount of data in its native, raw format. Data lake stores are optimized for scaling to terabytes and petabytes of data. The data typically comes from multiple heterogeneous sources, and may be structured, semi-structured, or unstructured.

Can we store tables in data lake? ›

So data lakes can store structured, semi-structured and unstructured data. Structured data would include SQL database type data in tables with rows and columns. Semi-structured would be CSV files and the like.

What are the components of lakehouse architecture? ›

Introduction to Data Lakehouse Architecture

The platform consists of multiple layers, including the object store, data layer, processing layer, semantic layer, communication layer, and client layer. Each layer provides open-source options that help maintain data portability, flexibility, and economic efficiency.

Who is the father of data lake? ›

Bill Inmon, widely considered the father of the data warehouse, heralds the birth of the data lakehouse, which makes efficient ML and business analytics possible directly on data lakes.

How do you put data into a data lake? ›

To get data into your Data Lake you will first need to Extract the data from the source through SQL or some API, and then Load it into the lake. This process is called Extract and Load - or “EL” for short.

What are the layers of a data lakehouse? ›

A Data Lakehouse is also the best solution for data governance and security. There are five components of a data lakehouse: data ingestion, storage, metadata, API, and the data consumption layer. As discussed in this article, data lakehouses can benefit organizations in different ways.

How is Lakehouse different from data lake? ›

A lakehouse is a new, open architecture that combines the best elements of data lakes and data warehouses. Lakehouses are enabled by a new system design: implementing similar data structures and data management features to those in a data warehouse directly on top of low cost cloud storage in open formats.

What is the difference between Delta lake and data Lakehouse? ›

Architecture: Data Lakehouse is a hybrid architecture that combines the best of data lake and data warehouse capabilities. Delta Lake, on the other hand, is a data management system running on Apache Spark. Reliability: Although data lakes are highly scalable and flexible, they are not known for their reliability.

What is the difference between data mart and data Lakehouse? ›

A data mart is a data warehouse that serves the needs of a specific business unit, like a company's finance, marketing, or sales department. On the other hand, a data lake is a central repository for raw data and unstructured data. You can store data first and process it later on.

Is Snowflake a data lake or data warehouse? ›

Snowflake Has Always Been a Hybrid of Data Warehouse and Data Lake.

What is another name for a data lake? ›

A data lake may also be referred to as a schema-agnostic or schema-less data repository.

What is the difference between CDP and data lake? ›

As mentioned above, CDP offers ready-to-use data for analysis while data lakes only offer raw information that requires preparation in order to perform any analyses. This means data lakes can slow down decision-making when sales and marketing teams must wait for data sets to be processed.

What is Lakehouse architecture? ›

Lakehouse architecture combines the best features of the data warehouse and the data lake, providing: - Cost-effective storage. - Support for all types of data in all file formats. - Schema support with mechanisms for data governance. - Concurrent reading and writing of data.

Is Databricks a data Lakehouse? ›

The Databricks Lakehouse Platform combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance and performance of data warehouses with the openness, flexibility and machine learning support of data lakes.


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