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Modern Data Lakehouse
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Streamlining Business Operation with the Modern Data Lakehouse

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Imagine being able to explore all business data at once with self-service access to it wherever it may be. Imagine being able to respond to pressing business questions quickly without having to wait for data to be located, exchanged, and consumed. Imagine being able to autonomously uncover deep new business insights from both structured and unstructured data working together, all without needing to request data sets.

Data Lakehouse is a relatively new concept in the world of modern data architecture. It is a combination of a data lake and a data warehouse, merging the benefits of both data storage solutions into one. This innovative approach is becoming increasingly popular among organizations due to its ability to handle both structured and unstructured data at scale, providing enhanced data management, governance, and analytics capabilities.

According to a recent study, the Data Lakehouse market is expected to grow at a CAGR of 24.1% from 2020 to 2025. This growth can be attributed to the increasing demand for big data analytics solutions, the need for data management and a rapid increase in the adoption of cloud-based data solutions.

The question is what makes a data lakehouse different from other data management solutions, how can it fit into your existing architecture, how can it fit into your existing architecture, and how can it help your business make the most of your data?

This blog will help you discover what differentiates cloud data lakehouse services from a data warehouse and discuss a pragmatic approach to data lakehouse architecture. Also, we will learn if it is an ideal solution for your business.

What is a Data Lakehouse?

A Data Lakehouse is a hybrid data storage and processing platform that combines the best of both traditional data warehousing and big data lake technologies. It is designed to address and resolve some of the limitations of traditional data warehouses and data lakes. A data lakehouse is a modern data architecture that is designed to store, process and manage all data, structured and unstructured, at any scale in a centralized repository.

In a data lakehouse, data is first ingested into a data lake, where it can be stored in its raw form. This allows organizations to store large volumes of data at a low cost, without worrying about the structure or format of the data.

Once the data is ingested, it can be transformed and structured using a set of tools and technologies, and then loaded into a data warehouse. This allows organizations to perform complex analytics and run business intelligence queries on the data, using SQL-based tools and techniques.

The Data Lakehouse provides several key benefits to organizations, including:

Improved Data Governance: The Data Lakehouse provides a centralized repository for all data, making it easier to manage data quality and security, and ensure data governance.

Faster Time to Insights: The Data Lakehouse provides fast query performance, making it easier for organizations to explore and process their data, and extract insights.

Better Handling of Unstructured Data: The Data Lakehouse is designed to handle both structured and unstructured data, making it easier for organizations to extract insights from a broader range of data sources.

Lower Total Cost of Ownership: The Data Lakehouse provides a centralized repository for all data, reducing the need for multiple data storage and processing platforms, which can help lower the total cost of ownership.

One example of a company using a Data Lakehouse is Uber. Uber collects and stores massive amounts of data from its ridesharing and food delivery platforms. The Data Lakehouse provides a centralized repository for all this data, making it easier for Uber to extract insights and make data-driven decisions.

Another example is Netflix, which uses a Data Lakehouse to store and manage all its customer data, including viewing habits and preferences. The Data Lakehouse provides Netflix with a centralized repository for all this data, making it easier for the company to extract insights and make data-driven decisions.

Attraction Growth Opportunities CAGR - infographic Picture

Recent Gartner report indicates that the Data Lakehouse market is expected to grow rapidly over the next several years, with the global Data Lakehouse market size expected to reach $3.3 billion by 2024, growing at a CAGR of 28.3% from 2019 to 2024.

Data Lakes vs Data Warehouse: What are the Differences?

How is a Data Lakehouse Different from a Data Lake or Data Warehouse?

The Data Lakehouse combines the benefits of both. It allows for the raw data to be stored in its original format, making it easy to access and analyze, but also provides the structure needed for efficient querying and analysis. This structure is achieved through data cataloguing and indexing, which allows for faster performance and improved governance.

A Data Warehouse is a centralized repository that collects, integrates, and stores vast amounts of data from various sources, including transactional systems, operational databases, and external sources. It enables organizations to have a single view of their data and provides the ability to analyze and report on the data in a meaningful and actionable way.

However, despite its many benefits, Data Warehouses also have their limitations. Some of the most significant limitations include:

Cost: Setting up and maintaining a Data Warehouse can be expensive, particularly for organizations with limited budgets. The hardware, software, and staffing costs involved in setting up a Data Warehouse can be prohibitively high for some organizations.

Complexity: Data Warehouses can be complex to set up and manage, particularly for organizations that are new to the technology. The process of integrating data from different sources and transforming it into a format that can be easily analyzed can be time-consuming and challenging.

Data Quality: Data Warehouses rely on the quality of the data that is fed into them. If the data is incorrect, incomplete, or outdated, the results generated from the Data Warehouse will be unreliable. This can result in poor decision-making and can have serious consequences for the organization.

Scalability: As the volume of data grows, Data Warehouses can become increasingly difficult to manage and maintain. This can result in longer processing times and increased costs.

Data Latency: Data Warehouses are designed to store historical data, which can be a significant limitation for organizations that need real-time data. The process of extracting, transforming, and loading data into a Data Warehouse can take time, and by the time the data is available, it may be out of date.

A data lake is a centralized repository that enables organizations to store large amounts of structured and unstructured data at any scale. The data lake is designed to handle a variety of data types, including transactional data, operational data, log data, social media data, and machine-generated data.

The main advantage of cloud data lake solutions is that it provides a cost-effective and scalable solution for storing large amounts of data. This allows organizations to store data for a longer time, making it easier to analyze data trends over time and make informed decisions.

However, as with any technology, a data lake also has its limitations. Here are some of the most significant limitations of a data lake:

Lack of data governance: Data lakes tend to be large, and without proper governance, it can be challenging to identify, track, and manage the various data sets within the lake. Data quality can suffer if there is no control over data ingestion, transformation, and management.

Complex data integration: Integrating data from various sources is one of the key objectives of a data lake. However, the process of data integration can be complex and time-consuming, especially when the data sets are vast and diverse. Ensuring that the data is properly organized, tagged, and indexed is crucial to its accessibility and usability.

High costs: Implementing and maintaining a data lake requires significant investments in infrastructure, storage, and data management tools. Furthermore, as the amount of data stored in the lake increases, the costs of managing and processing that data also increase.

Difficulty in data discovery: Finding the right data within a data lake can be a challenge, especially if the data is not properly organized, tagged, or indexed. Users can waste a lot of time trying to locate the right data, which can impact their productivity and effectiveness.

Lack of real-time data processing: Data lakes are designed for batch processing, and as such, they may not be suitable for real-time data processing. If you need to analyze and act on data as it flows in, you may need to supplement the data lake with other data processing technologies.

Security concerns: As data lakes are designed to store large amounts of data, they can become vulnerable to security breaches. Ensuring that the data is secure from unauthorized access, hacking, or data leaks requires a comprehensive security framework, including data encryption and access control.

How Does a Data Lakehouse Improve on the Data Warehouse & Data Lake

As businesses increasingly rely on data to make informed decisions, the need for robust data management platforms has become essential. Traditional data warehouses have their limitations, making it challenging to scale up, be agile, and cost-effective.

However, with the introduction of data lakehouses, businesses can have the best of both worlds. In this blog, we will explore how data lakehouses are improving on the limitations of traditional data warehouses.

One of the significant challenges with traditional data warehouses is their scalability. As data grows exponentially, it becomes increasingly challenging to store, process, and retrieve the data efficiently. This results in longer query times, which slows down the business processes.

With a data lakehouse, businesses can leverage cloud-based storage and computing power to scale up quickly and cost-effectively. With cloud-based data storage and computing, businesses can expand or shrink their storage and computing resources based on demand, making it a scalable and cost-effective solution.

Businesses today require agility to adapt quickly to the ever-changing market demands. Traditional data warehouses take a long time to set up, implement, and modify, resulting in longer lead times for new initiatives. With data lakehouses, businesses can quickly set up, integrate, and modify data pipelines.

This enables businesses to stay agile and adapt quickly to changing market demands. Additionally, with data lakehouses, businesses can store raw data and transform it on-the-fly, enabling businesses to access, analyze, and act on real-time data.

Data warehousing can be expensive, both in terms of hardware, software, and human resources. With traditional data warehousing, businesses often have to invest in expensive hardware and software licenses upfront. Additionally, businesses need skilled professionals to manage and maintain these systems, which can add up to the total cost of ownership.

Data lakehouses, on the other hand, offer businesses a cost-effective alternative. With cloud-based data storage and computing, businesses can pay for what they use, making it a more cost-effective solution. Additionally, businesses can store raw data, eliminating the need for expensive ETL (Extract, Transform, Load) processes.

Planning to streamline your data science efforts?

Shedding Light on Data Lakehouse Architecture

A Data Lakehouse architecture is built on three key components:

Data Storage: The data storage layer is the foundation of the Data Lakehouse architecture. It is used to store large volumes of raw data from various sources such as log files, sensors, and social media. This data is stored in its native format without being transformed, making it easier to access and query.

Data Warehouse Architecture - infographic Pic

Data Processing: The data processing layer is responsible for processing and transforming data into a format that is suitable for analytics. This layer can use various tools and technologies such as Apache Spark, Apache Flink, and Apache Beam to process the data.

Analytics Layer: The analytics layer is the top layer of the Data Lakehouse architecture. It is responsible for providing fast and cost-effective access to data for both batch and real-time analytics. This layer can use tools and technologies such as Apache Druid, Apache Kylin, and Apache Hive to provide fast and efficient analytics.

Overall, the Data Lakehouse architecture provides a unified platform for storing, processing, and analyzing large volumes of data from various sources. This architecture enables organizations to turn data into actionable insights and make data-driven decisions faster and more cost-effectively.

Technology on the Floor

Let’s look at some of the technologies that offer Data Lakehouse:

Snowflake is a cloud-based data warehousing solution that provides a platform for storing and processing vast amounts of data. It offers a fully managed and scalable data warehousing solution that enables businesses to store and process massive amounts of data.

Amazon Web Services (AWS) is a cloud-based computing platform that provides a wide range of data management solutions. Amazon Redshift and Amazon S3 are the two major offerings for cloud Data Lakehouse solutions.

Google Cloud Platform (GCP) provides a range of data management solutions that include BigQuery, Google Cloud Storage and Dataproc. It offers a fully managed, cloud-native data warehousing solution that enables businesses to store and process massive amounts of data.

Microsoft Azure is a cloud-based computing platform that provides a range of data management solutions. Azure Data Factory, Azure Blob Storage and Azure Databricks are some of the key offerings for Data Lakehouse solutions.

Azure Data Lakehouse is implemented through the integration of Azure Data Lake Storage and Azure Synapse Analytics. It combines the features of a data lake and a data warehouse, allowing users to store and analyze large amounts of data in real-time. The process begins by ingesting raw data from various sources into Azure Data Lake Storage.

Data is then processed and transformed using Azure Synapse Analytics, allowing for querying, modeling, and reporting. The resulting data is then stored in the lakehouse, ready for further analysis or machine learning. Azure Data Lakehouse provides a scalable and flexible solution for managing big data, with support for various programming languages and tools.

These are some of the technologies that offer cloud Data Lakehouse services. Businesses can choose the technology that best suits their needs based on their data storage and processing requirements.

Data Lakehouse: Is It an Ideal Solution for Your Business?

The data lakehouse solution can be a right fit for a business if it caters to the following requirements:

  • Large data volumes from disparate sources
  • Performs business intelligence and advanced analytics
  • Need for real-time and batch data processing
  • Scalable, cost-effective, and flexible storage solution

Integration of data pipelines to simplify data movement between different systems

However, the suitability of a data lakehouse solution for a business ultimately depends on its specific use case and data management requirements. It is recommended to assess your business needs and evaluate various solutions before choosing the final one.

Get Expert Help from Polestar Solutions to determine if data lakehouse is the best fit for your organization.

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