The world is witnessing an explosion of big data and analytics, creating an immense market need for data governance and security. All these factors are leading to unprecedented growth in the creation and management of data warehouses, data pools, and data lakes for businesses. According to recent reports, the data lake market is expected to grow at a 24% CAGR, to reach $25.49Bn by 2029.

But what is a data lake? It was in 2010 when James Dixon, the CTO of Pentaho, coined a term that has ever since come a long way. Starting from Hadoop storage or Hadoop distributed file system or HDFS (infamous for data swamp), traversing through ups and downs, the concept has finally reached today’s stage of explosion.

What are data lakes?

A data lake is a compact repository for storing structured data as well as semi-structured data and unstructured raw data at any scale. The data can be stored in its raw, original format without the need for any prior organization or categorization, making it flexible for data scientists or personnel associated with data management to store large volumes of diverse data types. Multiple teams can access this data easily to perform big data analytics and data science operations. The data from data lakes can be transformed and structured as needed for specific analytics use cases, rather than having to design a data model upfront. This approach makes it easier to gain insights from all types of data assets, providing a more comprehensive view of the organization's data structures.

Why do modern businesses require data lakes?

Every business is collecting enormous amounts of consumer data. But more than the volume, it's how they are utilizing the same that makes all the difference. However, data engineers often find it challenging to conduct big data processing in a seamless, effective way, owing to multiple data types and data sets, complex data management systems, and data silos. Besides, it can drain an enterprise of its much-valued capital reserve.

Data lakes, on the other hand, are often built on low-cost hardware, taking away the cost woe. Data lake solutions can then provide end-to-end support in data processing, data categorization, batch streaming data, analytics, and improved data integration while maintaining data quality at scale. Here are the top 5 ways data lakes impact the modern business landscape:

  • Big data analytics: With the growth of data volumes, traditional data storage and processing systems have become inadequate. Data lakes allow organizations to store and process large volumes of structured and unstructured data, making it easier to perform big data analytics on the data generated using data lake query engines and gain insights.
  • Flexibility: Data lakes allow for storing data in its raw form, without having to pre-define a specific structure or data model. This makes it easier for organizations to store and analyze diverse data types and quickly adapt to changing business needs.
  • Integration: Data lakes can integrate data from various sources, including IoT devices, social media, and transactional systems, making it easier to gain a comprehensive view of the organization's data.
  • Cost-effective: Data lakes can be more cost-effective than traditional data warehousing solutions, especially for organizations with varying storage and processing needs.
  • Improved Decision-making: By providing a centralized repository for storing data, data lakes make it easier for organizations to access and analyze data, leading to improved decision-making and increased competitiveness.

Overall, data lakes play a crucial role in helping organizations leverage their data to drive business value and remain competitive in today's data-driven landscape. This is all the more crucial for organizations that offer or utilize data analytics as a service.

What is a data lake vs a data warehouse?

A data lake and a data warehouse are both systems used to store and manage large amounts of data, but they differ in several key ways:

  1. Data ingestion: A data lake architecture allows organizations to store data in its raw form, without having to pre-process it, while data warehouses require data to be cleaned, transformed, and structured before it can be stored.

  2. Data structure: Data in a data lake is stored in its raw format, without a specific data model, while data in a data warehouse is stored in a structured manner, based on a pre-defined data model.

  3. Data access: Data in data lake solutions is typically accessed through batch processing or real-time processing for big data analytics, while data in a data warehouse is primarily accessed through structured query languages (SQL) for reporting and business intelligence (BI) purposes.

  4. Scalability: Data lakes are designed to be highly scalable, allowing organizations to store and process large amounts of data, while data warehouses are typically more limited in terms of scalability.

  5. Cost: Data lakes can be more cost-effective than data warehouses, especially for organizations with varying data storage and processing needs, as they allow organizations data storage in its raw form, without having to pay for the processing required to clean and structure the data.

In short, while both a data warehouse and data lake serve as repositories for storing and managing data, they differ in terms of the type of data they store, how the data is processed, and how it is accessed. Data lakes are often used for big data analytics and data science, while data warehouses are typically used for reporting and business intelligence.

What is a data lake on cloud?

A data lake on cloud refers to a centralized repository for storing organizational data in its raw format on a cloud computing platform such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). To cater to this growing demand, all the leading hyperscalers provide storage services for data lakes. For example, GCP offers Google cloud storage for enterprises to build their data lake on cloud.

Data lake on-prem vs cloud is a debate happening over the years as enterprises attempt to wring in as much value from data assets as possible. Traditional data lakes are set up and managed within an organization's own IT infrastructure rather than in the cloud. It works by collecting data from various sources, storing data in Hadoop Distributed File System (HDFS), processing data using tools such as Apache Spark, Apache Hive, Apache Pig, etc., analyzing data using tools such as Apache Impala, Apache Drill, and Apache Mahout, and finally data visualization using Tableau, QlikView, Power BI.

The benefits of using data lake on cloud include scalability, cost-effectiveness, accessibility, integration with other cloud services, and robust security features provided by cloud providers. By storing data in a cloud data lake, organizations can access and analyze their data from anywhere, making it easier for teams to collaborate and share data. Additionally, cloud data lakes can be integrated with other cloud services, such as data processing tools, machine learning platforms, and data visualization tools, making it easier to perform big data analytics and data science operations.

How to build data lakes on cloud?

Now, let's take a look at building a data lake on cloud. This involves several steps, including:

  1. Define business requirements: Start by defining the business requirements for your data lake, such as the types of data you want to store, the frequency of data updates, and the type of analytics you want to perform.

  2. Choose a cloud provider: Choose a cloud provider, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP), that meets your business requirements and offers the features and services you need for your data lake.

  3. Set up the data lake: Create the data lake using the cloud provider's data lake solution, such as AWS Lake Formation, Azure Data Lake Storage, or GCP BigQuery (the data lake query engine from Google Cloud), and set up the necessary data storage and data processing services.

  4. Store data in raw format: Store data in its raw format without the need for pre-processing or categorization, making it easier to store and analyze diverse data types.

  5. Integrate with other cloud services: Integrate your data lake with other cloud services, such as data processing tools, machine learning platforms, and data visualization tools, to enable big data analytics and data science operations.

  6. Implement data security and compliance: Implement robust security and compliance features, such as data encryption and access control, to protect sensitive data stored in the data lake.

  7. Monitor and maintain the lake: Monitor the data lake to ensure it is functioning optimally and make necessary updates and maintenance as needed.

Building a data lake on cloud requires careful planning and attention to detail, but the end result is a centralized repository for storing and analyzing large amounts of diverse data, enabling organizations to make data-driven decisions and gain competitive advantages.

What are the benefits of data lakes on cloud?

Building data lakes on cloud platforms offer several benefits, including:

  1. Scalability: Cloud platforms provide the ability to quickly scale up or down the storage and processing capacity of a data lake, making it easier to handle sudden spikes in data volume.

  2. Cost-effectiveness: By paying only for what you use, data lake on cloud can be more cost-effective than on-premise data lakes, especially for organizations with varying storage and processing needs.

  3. Accessibility: Data lake on cloud can be accessed from anywhere with an internet connection, making it easier for teams to collaborate and share data.

  4. Integration: Many cloud platforms offer integration with a wide range of data sources and tools, making it easier to collect, store, and analyze data.

  5. Security: Secure data lake is the need of the hour. Cloud providers typically have robust security measures in place to protect data stored in multi-cloud data lake, including encryption, access control, and disaster recovery.

Key business applications of data lakes on cloud

Data lakes on cloud have several business applications, including:

  1. Customer analytics: By integrating customer data from various sources, such as transaction systems, social media, and marketing platforms, organizations can gain insights into customer behavior and preferences, helping to improve customer engagement and drive sales.

  2. Fraud detection: Data lakes on cloud can be used to store and analyze large amounts of transaction data, making it easier to detect fraudulent activities and reduce the risk of financial losses.

  3. Supply chain management: Data lakes on cloud can be used to store and analyze supply chain data, such as inventory levels, shipping information, and production data, helping organizations to optimize their supply chain operations and reduce costs.

  4. Marketing analytics: Data lakes on cloud can be used to store and analyze marketing data, such as customer behavior data, website analytics, and social media data, helping organizations to improve their marketing campaigns and reach customers more effectively.

  5. Predictive maintenance: By storing and analyzing machine data from IoT devices in a data lake on the cloud, organizations can predict equipment failures and take proactive measures to reduce downtime and improve efficiency.

  6. HR analytics: Data lakes on cloud can be used to store and analyze HR data, such as employee performance data, payroll information, and training data, helping organizations to improve their HR processes and increase employee engagement.

How can different industries leverage data lakes on cloud?

Industry

Applications

Insurance

  • Pricing and underwriting
  • Settle claims
  • Monitor houses and health
  • Secure access to information
  • Create and deliver unique solutions
  • Improve customer experiences

Fintech and banking

  • Integrate data lakes with analytics and visualization tools
  • Accelerate data-to-insightful report cycle
  • Keep pace with evolving compliance landscape
  • Aid customers with financial decision-making
  • Automate compliance reporting
  • Boost loan book profitability
  • Enhance collection efficiency

Energy sector

  • Stream multiple content types into one source
  • Automate end-to-end records management and compliance
  • Rapid search of content for request-response
  • Quick information sharing and consolidation during M&A
  • Extract value and insights from data
  • Optimize and improve operations

Food and beverages

  • Predict customer demands
  • Quality control in production
  • Enable faster delivery for increased shelf life
  • Inventory management
  • Improve marketing strategies

Utilities

  • Draw conclusions faster

Conclusion

The future of data lakes on cloud looks bright as more and more organizations are adopting cloud computing and big data technologies. Some of the key trends and predictions in the future of data lakes on cloud include an increased adoption due to rising demand, improved integration with other cloud services, an increased focus on advanced analytics, as well as a heightened importance of security and compliance to protect sensitive data on cloud, all leading to a scope of greater collaboration between teams as data becomes more accessible and shared across organizations. With Cloud4C, the world’s leading application-focused Cloud MSP, businesses find it easy to master their data capabilities including storing data, modernizing databases, blueprinting data advancements, and attaining data-based business intelligence. Read on our overall data modernization and data analytics capabilities.

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Author
Team Cloud4C
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Author
Team Cloud4C

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