Data lakes and data warehouses both house data, so what is the difference?
Data lakes are used to store all types of data. This includes historic data for archiving, or fresh data that is currently in-use or being generated by business services or systems. Until recently, storage capacity was too expensive to accommodate such large-scale big data projects.
Now the cloud has reduced storage costs to the point where data lakes are viable and cost-effective, especially when weighted against the value of the insight that can be generated.
Data lakes also support all data types. When storing data, there are three main formats:
Unstructured – Often referred to as raw data, unstructured datasets are not arranged according to a pre-set data model or schema type. This is the most common type of data and is generated by all systems and services continuously. Unstructured data types might include alphanumeric text strings, raw log files, photos, video, and audio. These data types are stored in a non-relational database management system (non-RDBMS) such as NoSQL, which models data in a non-tabular format for increased flexibility.
Semi-Structured – Unlike raw unstructured data, semi-structured datasets include tags and markers to separate semantic elements and enforce hierarchical records or fields. This may include email messages or photos, where each item has an associated email address or geolocation tag—which is structured data, but is otherwise unstructured when stored in the database. In short, semi-structured data could be viewed as unstructured data with associated metadata.
Structured – Structured datasets follow a data model, such as using rows or column fields, to establish a data structure as designed by a database engineer. Structured data can be added to a relational database management system (RDBMS) to create data relations using languages like SQL, or to create object relations in an object-relational database management system (ORDBMS) using services like Oracle DB.
Data lakes can accommodate all three types of data at once. Enterprises do not need separate non-RDBMS, RDBMS, or ORDBMS solutions for each data type. The data lake can house everything in a single, centralized repository.
Furthermore, advanced analytics tools enable greater leveraging of semi-structured and unstructured datasets in a data lake thanks to artificial intelligence (AI) and machine learning (ML). For decades, this data was forgotten due to high performance requirements and a lack of processing capacity—something which cloud-based AI and ML has resolved. Now, hidden insights in unstructured and semi-structured are coming to the forefront, enabling data-driven business understanding of services, systems, and customers.
Third we have accessibility. By centralizing your datasets in a data lake, every user or system is referencing a single source of the truth (SSOT). This eliminates duplicate data entries, increases data validity, and reduces the prevalence of human error. Furthermore, data lakes are designed to handle simultaneous access by multiple users or services. One user could access structured data to compile reports, and an analytics tool could access unstructured data to generate insight—with all these use cases being supported simultaneously by the data lake architecture.
Contrarily, a data warehouse has a more specific purpose. It is used to store structured and filtered data that has already been processed via an extract transform load (ETL) pipeline.
Imagine a real-life warehouse. You have shelves which house the data, and this data can come from a variety of verified sources, much like a product supplier. Every piece of data is checked-in and checked-out, like an inventory system in a warehouse, requiring processing and validation to protect data integrity.
A data lake simply lacks this structure, ingesting any and all data from any source without extensive pre-processing or structuring. Data warehouses follow what’s called a schema-on-write process to structure data by using a pre-defined configuration which limits flexibility.
As a result, to change this configuration and structure requires data scientist input and redevelopment of integrated services and systems to support the new structure, leading to short-term disruption and higher workloads.
According to the research firm Aberdeen, organizations that have moved to data lakes outperform similar companies by nine percent in organic revenue growth. This competitive advantage comes from a data lakes ability to:
Leverage all your corporate data - Cloud data lakes allow you to centralize your data sources across services, systems, and communication channels with robust security and ease-of-use. By creating relations between previously unrelated information in a data lake, you can develop insights at pace, and with more accuracy than a data warehouse alone.
Feed your machine learning - Pairing inexpensive and scalable cloud storage with cloud burst processing gives machine learning algorithms the juice they need to comprehensively analyze all your data. With instant and virtually-infinite scalability, your AI processes will have ample compute and storage capacity ready for long-term insight generation.
Preserve your raw data for future analysis - By storing data natively in a cloud-based data lake, you preserve the raw format of that information for future analysis. This raw data can be leveraged via more powerful analytics tools through software integrations, and fully-complies with various legal and regulatory obligations.
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Cost savings - With the scale at which cloud service providers (CSPs) operate, cloud compute power and storage has become much cheaper than the equivalent on-prem solution. This affordability creates a virtuous circle, allowing you to store more and more raw data to perform more complex analyses.
High-availability and disaster recovery - The cloud is founded on the principle of redundancy. By operating clusters in the cloud, processes and workflows are segmented and distributed. This leads to better data availability, better latency with global or localized data access, and more resilience to server-specific outages. Additionally, there is a guarantee in the form of a CSP service level agreement (SLA) that ensures data integrity and upholds accessibility for all users and services.
Data is constantly being generated across your business systems, services, and communication channels:
Server log files
Social media interactions
Internet of Things devices
The challenge isn’t storing this data, however. The real challenge is revealing actionable trend lines and delivering reporting insights in quick and concise manner. To do that, you need full data integration across your IT ecosystem, storing this data in a coherent and centralized pool which can be easily accessed by advanced analytics processing tools.
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Traditionally, it would take years and millions of dollars for a business to construct this kind of big data platform. Trianz developed EVOVE to expedite this process, automating extract transform load (ETL) operations in real-time during data migration.
The result is rapid source to target data migration, replicating the formatting, structures, and query frameworks from the source database in the target data lake. EVOVE reduces errors, improves integrability, and saves money during big data migrations—helping you to start leveraging your new data lake at speed.
Apache Hadoop is an open-source framework for processing unstructured big data sources. A Hadoop pipeline can be constructed to the target data lake, ingesting unstructured big data sources that can reach petabytes (PB) in size without incurring significant costs.
Hadoop does this by managing data staging and pre-processing. Each node in a Hadoop cluster processes the data logic using a data locality concept, rather than processing the data itself. This means the source data is not transmitted, which would otherwise require massive amounts of bandwidth and processing. In short, these nodes and clusters reduce network bandwidth requirements, increase the ease and speed of scaling, and make the Hadoop ecosystem highly fault-tolerant through cross-node failovers.
Trianz recommends solutions like Hadoop for unstructured big data analytics. Our data experts can help you develop and deploy an Apache Hadoop architecture to enable advanced and cost-effective analytics functionality.
AWS is a leading cloud services provider (CSP). As an AWS Service Advanced Partner, Trianz brings deep industry experience and best-in-class deployment frameworks to help our clients seamlessly transition from a data warehouse to a data lake.
Amazon Data Lakes are based on the highly-popular Amazon S3 bucket storage service. This service is lauded for delivering high data availability and reliability at a reasonable cost.
More machine learning (ML) happens on AWS than any other platform in the world, with more than 10,000 individual customers at present. This high adoption rate is due to AWS offering highly-advanced ML algorithms with swift and efficient customer support.
AWS and Trianz both hold broad experience across every industry vertical. Additionally, Trianz’ expertise ensures that your data lake is both secure and compliant with data regulations such as:
AWS data lakes integrate natively with powerful analytics services on AWS, including:
Our experts can develop and deploy an industry-leading data lake solution with minimal disruption to your overall business processes. See how our other clients have benefited from data lakes by reading our use cases and success stories.
Our experts can also deliver data lake transformations on Microsoft Azure. With aggressive pricing and support, Microsoft makes it easy to integrate your current Windows or Azure Directory assets with an Azure data lake.
Microsoft was a pioneer with early relational database management systems (RDBMS), delivering advanced functionality that transformed data-based workflows. Our experts can develop and deploy these data technologies, connecting or operating almost any data store in conjunction with an Azure data lake.
Azure offers broad support for different database storage formats, including:
Relational Database Management Systems (RDBMS)
Non-Relational Database Management Systems OR NoSQL (Non-RDBMS)
Object-Oriented Databases (OODBMS)
Hierarchical Databases (such as MariaDB)
Schema-free JSON Data Search Engines (such as Elasticsearch or Splunk)
By leveraging industry-standard platforms like Hadoop and providing native integration with Azure Active Directory, Microsoft and Trianz can make connecting data lakes to your internal systems safe and smooth. You also get the ability to leverage the Cortana Intelligence Suite with:
Data Lake Analytics
A browser-based interface, commonly referred to as a web application, delivers the power of Azure to any user, in any location, on any device with an internet connection.
Microsoft offers support for non-Microsoft data inputs, with over 70 supported data source connectors to ingest and store every bit of data in your data lake.
Trianz is a Microsoft Gold Certified Partner, and has been an Azure Managed Services Partner since 2015. This enables us to completely transform your data operations via data lake implementation on Azure.
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