With the growth of unstructured data, organizations must consider data lakes when making essential decisions. By 2025, 80% of all data will consist of non-structured forms, and sufficient scalability, integration, deployment options, and automated governance must be implemented for trustworthiness. Along with this comes the requirement for strategically allocated storage that best serves current needs to stay up-to-date with the constantly changing landscape. Looking to find out more? Take a look at this article which will show you what you need to know about navigating the journey to successful Data Lake Automation.
What is a Data Lake House?
A data lake house provides the benefits of both a data lake and a data warehouse. It has an open, flexible architecture that can be easily scaled to store and process large amounts of unstructured, semi-structured, and structured data. The implementation of data and tools together in one more extensive system further enhances its ability to enable predictive analytics and machine learning for making more informed decisions. Businesses can effectively utilize their big data assets with a data lakehouse for purposeful business impact.
Data lakehouses combine the data warehouse model and a new, high-performing SQL query engine applied to data lakes. Metadata layers and streamlined data governance help to optimize analytical processes. Additionally, businesses can leverage built-in aggregations and metrics on their data models for use in BI tools. To build a successful data lakehouse, companies should aggregate all incoming data from various channels and unify them for adequate access and organization.
The Benefits Of Data Lakehouse
Modern data lakehouse architectures offer a range of advantages that make them a sound investment for enterprise data storage initiatives. These benefits include scalability and cost savings, improved resource utilization, enhanced reliability and security, faster access to data, and greater flexibility in managing workloads separately. These advantages combine to create an ideal solution for businesses investing in sustainable data management practices.
- Data quality made easy with dual-layered architecture: A data lakehouse combines the power of a warehouse layer embedded over an enterprise data lake. The schema in this architecture enhances data quality control and makes faster business intelligence and reporting possible.
- Data Drift Reduction: A data lakehouse architecture can help reduce data drift and its associated challenges by eliminating the need for multiple copies of the same data. This enables organizations to save time and increase the efficiency of maintaining up-to-date data.
- Optimized querying: This architecture encourages faster interactive querying and better data democratization, allowing users such as data scientists, engineers, and analysts to access the relevant data quickly. Its faster time-to-insight cycle allows for more informed decisions than traditional architectures.
- Reduced Administrative Burdens: By utilizing a data lakehouse architecture, companies can save valuable time and resources when storing, handling, and processing data. As such administrative burdens are reduced through a single platform approach to data management, firms can enjoy lower maintenance costs while reaping the rewards of improved business insights delivered in faster timelines.
- Data governance made simple: By utilizing a data lakehouse, teams can meet rigorous demands for data integrity and security, permitting the utilization of advanced features, including audit logging and access control. This single-source approach offers unprecedented control over crucial metadata.
- Lower Data Redundancy: Organizations can benefit from the data lakehouse architecture by avoiding redundant data copies. Compared to data lakes and data warehouses, the need for multiple documents necessary in implementation is eliminated, thus leading to less risk of error due to data drift.
- Enhanced Scalability: A data lakehouse allows organizations to scale data and associated metadata quickly. This enables businesses to quickly understand their data to make informed, real-time decisions supporting their analytics initiatives. High scalability is one of the key benefits of using a data lakehouse.
An Overview Of Latest Data Lakes Trends
Enterprise data lake solutions are excellent for organizations that store large volumes of unstructured data. Data lakes enable the collection and storage of data across multiple sources in various formats while maintaining the quantity and quality of the stored data. Data lakes are often considered cost-effective compared to other methods of storing legacy data, and they can be used to facilitate exploratory analysis.
Enterprises increasingly recognize the value of data lakes for their digital transformation initiatives. For successful management and deployment, it's crucial to have adequate data discovery methods, secure cloud platforms such as Microsoft Azure, Snowflake, Hadoop, Google Cloud, or AWS for insight delivery, and to ensure privacy and security measures are put in place.
The pandemic is prompting companies to accelerate their digital transformation journey at a faster pace. A successful transition requires defined strategies and roadmaps that identify analytical insights needs, the ability to shift between cloud environments without compromising quality or standards set forth by regulators, and Data Lake Automation tools that can aid with these tasks.
The Barriers To Implement The Full Potential Of Data Lakes
Organizations understand the importance of data lakes; however, many must pay more attention to their complexity. Data scientists must have the expertise to build and maintain them when encountering ongoing costs, diverse data sets, and security issues. Furthermore, companies need to address the common problem of data silos which can prove troublesome in efficient analysis.
- Data Lake Ingestion: Data lake ingestion is an essential puzzle piece. Bringing together large amounts of data from multiple sources into a unified format within a data lake implementation can be tricky, especially when the data changes quickly. This may lead to issues with data capture and latency in generating new insights. It's a significant undertaking that requires careful consideration.
- Big data causes issues with agility - The sheer size and complexity of data sets, combined with streamlining databases, makes it tough for data lakes to handle the workload efficiently.
- Data preparation and accessibility: Data preparation and accessibility can provide significant challenges when managing large amounts of data in an organization. Improper linking and limited information availability may improve data consistency and accuracy, positively impacting operations. To ensure a reliable model for data handling, it is crucial to identify critical connections between data sets and define what type of information should be accessible to personnel at all times.
Choosing The Right Data Lake Scalability, Integration, And Deployment Options
The enterprise data lake solutions are essential to gathering the most up-to-date and diverse data sources. It is a source for streaming audio, video, call log, sentiment, and social media data. This contributes to more complete insights required for AI, machine learning, and Data Science initiatives. To maximize the potential of the enterprise data lake, it must be scalable, integrated, and widely deployable to guarantee no uncollected or unused data. These features are core principles for successfully applying the data lake now and in the future.
For a data lake implementation to be considered enterprise-ready, it must have scalability, integration, and deployment options that can effectively handle its core duties. With such capabilities in place, the data lake can ingest any amount of data in real time and store cold historical datasets from a database or warehouse conveniently and at a low cost. This is typically done with commodity hardware clusters, ensuring reliable data ingestion takes place all the time.
Deploying a data lake with federation capabilities is always a good idea. It's much faster than relying on ETL or ESB processes, making it easier to manage data in silos. For better performance and more control, you can use something like IBM Db2 Big SQL as the SQL-on-Hadoop engine.
But every business is different, so having multiple deployment options helps. 45% of companies have at least one significant data workload running in the cloud, so you should look for solutions that offer on-premises, multi-cloud and hybrid deployments. This allows companies to make decisions that match their compliance needs (like putting the data lake behind an on-premises firewall) or efficiency needs (such as utilizing a pay-as-you-go cloud model). Hybrid environments make companies more agile by letting them mix and match locations as needed for any situation.
Automated Governance For The Data Lake
A successful data lake requires a robust governance framework that covers multiple areas, such as data integration and cataloging or traditional governance and self-service access. Data Lake Automation is vital for DBAs and data scientists so they can spend time focusing on more meaningful projects. Data integration initiatives can be significantly improved through federated databases – in-line quality policies and active metadata enforcement should be enforced to facilitate the process. According to experts, automation through AI and ML could reduce the task of integrating data by 45% by 2022.
Data cataloging provides organizations with an improved understanding of the data contained within their data lake. It helps define data in terms business professionals understand while allowing for better visual exploration and tracking of data lineage. Solutions like IBM Watson Knowledge Catalog can be used to take these capabilities to the next level, offering automated data discovery, ML-generated business glossaries, and the ability to detect potential risks in unstructured data through automated scanning.
Data governance is closely aligned with security, compliance, and audit readiness. This makes automation vital to decrease the amount of manual effort needed. Many products like IBM InfoSphere Information Governance Catalog should be used to automate the classification and profiling of data assets, enforcing data protection rules that anonymize or restrict access to sensitive information. Additionally, it should provide timely incident responses by flagging sensitive data, identifying issues, and enabling easy auditing.
A significant advantage of self-service data governance for data scientists is that it streamlines the process by automating the tedious tasks of data preparation and model building. Through automated AI data lake software solutions like AutoAI, data scientists can access clean data quickly, enabling them to arrive at meaningful insights in a fraction of the time it would take otherwise. Ultimately, this means timely and dependable results.
Automation Through Modern and Scalable Data Architectures
Data deployment environments and data formats are increasingly becoming more complex for enterprises. Hybrid environments, data lakes as a service, and service providers allow businesses to access pre-built cloud services and have them installed and managed by third parties.
Such services provide several benefits, including automated provisioning, advanced analytics on any size data, real-time ingestion at scale, scalable storage, integration & access services, implementation & go-live enablement assistance, metadata management & governance plans, end-to-end security measures with encryption, and much more. These offerings make it easier for companies to securely achieve their desired data objectives.
Enterprises can generate value from data lakes, but many need help determining the best approach due to software and processing issues or the sheer size of their lake. Automation, modern BI tools, and improved data architectures are driving changes in this landscape. Enterprises now know better what they want from their lake and how they obtain it. The data landscape will evolve according to customer and enterprise requirements, delivering significant benefits to organizations that use these technologies.
The Best Data Lakehouse Platforms To Choose
Amazon Web Services, Azure Databricks, and Snowflake are three of the top data lake providers lakehouse platforms for companies looking to optimize their data management projects. Organizations should carefully consider their particular requirements when deciding which platform to use.
AWS Data Lake
Amazon Web Services has a data lake solution that makes it easy for users to locate, tag, share, analyze, and govern the subset of data relevant to their business. This lake house AWS includes a console that allows users to search and browse datasets and a federated template that aids in integrating with Microsoft Active Directory. The solution automates the configuration of the necessary AWS services necessary for success and is also one of the best data lake providers in the industry.
Snowflake Cloud Data Platform
Snowflake's cloud data warehouse gives users standard SQL support and the ability to enter queries, delete entries, analyze data, run transactions, and join multiple tables. The best part is that no infrastructure or management is needed - all of this is done on Amazon Web Services. Snowflake's columnar database engine efficiently processes reports and analytics with optimized results for structured and unstructured sources such as JSON, Avro, and XML. This translates into faster crunches, more accurate metrics, and reliable results!
Azure Data Lake
Microsoft Azure Data Lake provides comprehensive capabilities to simplify data storage and processing across platforms and languages. This solution has enterprise-grade security, auditing, and support features that suit any organization. Furthermore, this system integrates with automated data warehouses and operational stores, making extending current business solutions easier. Additionally, YARN technology ensures the smooth operation of this system even in cloud environments.
Data lakes will become increasingly vital to organizations' data strategies as the years go by. To realize their true potential and create solutions that align with their business vision, enterprises must consider effective data architecture, digital maturity, and hosting environments. Automating tasks such as data collection, processing, quality checks, and governance can help enterprises save both time and money. Moreover, automation also ensures compliance with regulations while improving the accuracy of the collected data. The intelligent use of automation can improve an organization's overall efficiency and effectiveness.