As organizations ramp up their efforts to be truly data-driven, the rate at which they collect data continues to grow. However, many have struggled to gain actionable insights from this data due to the volume and variability they’re forced to contend with, which has proven difficult to manage using existing data warehouses and data lake architectures.
For many years, organizations relied on structured data in their data warehouses for all their business reporting and dashboard needs. However, since the rise of unstructured and semistructured data—data lacking any predefined format or model that comes in a myriad of file formats, including email, social media posts, presentations, and IoT sensors—many have begun employing a combination of data warehouses and data lakes for data analytics and storage, allowing for use of less precise data in more statistical and trends-based insights.
While data lakes have also helped us simplify data collection, this means data is often spread across multiple systems and often requires moving unstructured raw data from data lakes to the more structured data warehouses to perform traditional business intelligence analytics—a clunky and cumbersome process. Furthermore, this separates the data used for trends analytics from the precision data used to drive business dashboards and reports, hindering exploratory and ad-hoc analytics when reports and dashboards indicate an issue requires action.
To truly support modern business analytics, we need to easily move between data warehouse precision and data lake exploration, without missing a beat. Current infrastructure architectures don’t support this. So, the need for a better approach to data management has led to new solutions: cue the data lakehouse.
Data warehouses offer a single storage repository for structured data and provide a source of truth for organizations. However, organizations must structure and store data inputs in a specific format to be able to extract and efficiently query this data.
On the other hand, data lakes are flexible environments that can store both structured and unstructured data in its native form, enabling organizations to use this data to build artificial intelligence (AI) and machine learning models from large volumes of disparate data sets. Unlike data warehouses, however, data is not transformed before landing in storage, so it can quickly become overwhelming.
As its name suggests, a data lakehouse combines the structure and accessibility of a data warehouse with the massive storage of a data lake and is built to house both structured and unstructured data. Businesses can benefit from working with unstructured data while only requiring one data repository rather than both warehouse and lake infrastructure.
Data lakehouses also enable structure and schema like those used in a data warehouse to be applied to the unstructured data of the type that would typically be stored in a data lake. This means that data users can access the information more quickly and start putting it to work, be they data scientists or any employee that is increasingly seeing the benefits of augmenting themselves with analytics capabilities.
1. Transformative insights with advanced analytics
By design, a data lakehouse creates a single source of truth in a master repository, allowing organizations to uncover new ways of combining their structured and unstructured data. This means that by using technologies such as AI, data users—even those without degrees in data science—can easily unlock insights from any type of data. These new advanced analytics insights often create game-changing business models that can significantly reduce customer churn, massively improve operational efficiencies, reduce or eliminate fraud and security risks, and transform cost models making us more competitive and more effective.
2. Simplify compliance and increase data value with better data governance
Data lakehouses simplify and improve governance by consolidating resources and data sources that are built with standardized open schemes. This allows for greater control over security, metrics, role-based access, and other critical management elements, which significantly simplify efforts to keep up with regulatory compliance, ensure data security without limiting sharing, and ensure trust in data when driving new insights.
3. Reduce infrastructure and administration burdens with reduced redundancy
Because data lakehouses combine the functions of lakes and warehouses, they offer an all-purpose storage platform that can handle any type of data. As a result, organizations can move away from separated lake-warehouse models that see data duplicated to ensure accessibility. As a result, organizations can combine and consolidate infrastructure (on premises and cloud) resources, and reduce administrative complexity.
4. Improve overall TCO for analytics with increased cost-effectiveness
Data lakehouses are built with a modern cloud-native architecture that separates compute and storage, allowing for easy storage addition without the need to augment compute power, and easy autoscaling of compute to right-fit the power with the current need. This means that we’re no longer investing in “high watermark” compute and storage, which often leaves resources idle waiting to be needed. Instead , data lakehouses are inexpensive to scale because integrating new data sources (compute or storage) is an automated process—they don’t have to be manually fit with the organization’s data formats and schema, ultimately lowering the total cost of ownership (TCO) of the entire analytics landscape.
5. Increase flexibility with ability to choose best of breed tools
Data lakehouses are built with the goal to combine all data and make it available for various data users to use the data for advanced analytics. With the increase in the number of data users from data engineers, to data scientists, data analysts, streams engineers, data integrators, developers and more all demanding to use data with their choice of tools and engines. Lakehouses are becoming more Open to easily support wide variety of metastores, engines and tools to concurrently access and update the data without creating silos.
To help organizations unlock the power of data even faster, Cloudera has embraced and simplified the data lakehouse concept powered by Apache Iceberg, an open table format that provides multifunction analytics capabilities to your lakehouse. It enables fast and easy self-service analytics and exploratory data science on any type of data.
Designed to make both data practitioners and expert developers more productive, companies can deploy CDP in a lakehouse architecture to achieve faster time-to-business insight to drive innovation and stay ahead of the competition, unlocking the power of their data. To learn more watch our on demand webinar, Unify your data: AI and analytics in an open Lakehouse.
Navita Sood is Director, Product Marketing at Cloudera. She is a thought leader in data and analytics, helping companies transform their data architecture and adopt cloud technologies to unleash the potential of their data.