Senior Technology Professional – Innovation Group – Big Data | IoT | Analytics | Cloud @YASH Technologies
Organizations today face the insurmountable task of managing multiple data types coming in from a variety of sources. With increasing volumes of massive and heterogeneous data, business leaders find it challenging to deliver insights on time. The want is of a data storage and analytics solution that offers more flexibility and agility than conventional data management systems.
Serverless data lakes today are a new and increasingly popular way of storing and analyzing data – both structured and unstructured ones – in a single repository.
Given that data is stored in the form, it comes in (is ingested), there is no more the need to know what questions you need to ask beforehand or even convert them into a predefined schema. In this blog, we will touch upon what is driving serverless data lakes’ popularity, the different ways you can ingest, store and analyze your business data, and how you can draw valuable insights for a competitive edge through intelligent architectural designs and data governance.
Difference between traditional data warehouse and serverless data lakes
Traditional ‘big-data’ platforms take time to spin up a fully functional data platform. This is where serverless architectures benefit from significantly reduced complexities and operational costs, making them a good fit for data platforms. Serverless data architectures offer the ability to support ad-hoc querying of data, the flexibility of consumption, advanced analytics such as machine learning, along with aggregable real-time visualization of data to see a whole history of log files.
With any data lake, you should be able to support the following capabilities:
- Collect and store any data at scale and low costs
- Secure and protect all critical data stored in a central repository
- Find relevant data from the repository whenever needed
- Quickly and easily perform data analysis on datasets when needed
- Query data by defining the data’s structure at the time of use (schema-on-read)
It’s important to note the difference between data warehouses and lakes. A data warehouse expects its ingested data to be of a precise schema (schema-on-write model), meaning ETL operations (Extract, Transform, Load) must be run to extract any valuable insight from the data. To contrast, data lakes rely on the schema-on-read model, easing importing of raw data to the lake regardless of its structure – and in its purest and rawest form. Also, regardless of your architecture, a data lake is not meant to replace your existing warehouse of data, but rather complement it.
A few of the benefits to look for are:
- Cost-effective storage: Serverless Data lakes (such as AWS cloud, Azure Blob Storage, Amazon S3, etc.) let you store nearly unlimited amounts of data from any type and from any source. Given storing doesn’t require upfront processes/transformations, you can apply diverse schemas for data analysis and flexibly improve time-to-value. For example, on AWS, businesses pay only for the period or the capacity they use – and none for idle capacity – bringing down the cost drastically.
- Easy collection and ingestion: You can ingest data into your data lakes through a variety of services (e.g., AWS Kinesis) in real-time, in batches, and even connect on-premise software appliances directly for improved connectivity.
- Security and compliance: Given that you host your data lake on the cloud such as AWS, you get access to highly secure cloud infrastructure and a deep suite of security offerings designed to keep your critical data safe and in compliance with global standards such as HIPAA, PCI DSS, etc.
- Nearly zero-maintenance: Given that the underlying infrastructure is owned and operated by the cloud service provider, the maintenance burden is not on the customer – except for the need to have operational monitoring of the service.
- Default DR and BCP: Businesses do not need to plan separately for high availability of the data lake, as both business continuity plans (BCP) and disaster recovery (DR) exist as default in serverless platforms.
Advantages of choosing serverless data lake architecture
The primary advantage that Serverless data lake architecture offers is the ability to store objects in a highly durable, secure, and scalable manner with only milliseconds of latency for data access. You have the freedom to store any type of data – from web sites, business apps, and mobile apps to even IoT sensors. Some other advantages for seamless business visibility and use are:
- High availability and scalability: Distributable and automatically replicate data across data regions on a specific cloud environment (e.g., AWS S3 or Azure Blog storage)
- Secure and compliant: Get multiple encryptions of data for high security, as well as the ability to use machine learning to discover and protect sensitive data
- Seamless querying: Run analytics and machine learning for seamless queries
- Management flexibility: Classify, catalog, report and visualize your data usage easily; tag objects to see storage cost, consumption, security, etc. to create lifecycle and automation policies
Despite its promises, we know how daunting a proposition may seem to build a data lake. You may face difficulties in understanding what is required to begin, given that all the different and often costly options you can choose off-the-shelf from in the market.
Challenges in designing data lake architecture
The biggest problem with implementing data lakes is to be able to efficiently create storage and catalog your data in a way that can be queried and quickly resolved. You can implement data lakes leveraging anything from on-premises block storage based Hadoop (HDFS) system to cloud-native offerings that come with limitless storage such as Azure Blob Storage or AWS Simple Storage Service (S3).
While the issue is not very technical in its very nature, i.e., without a direct technical solution, the struggle is in applying a complete data management mindset to your company data. Implementing a modernized (cloud-native) serverless architecture for a data lake, for example, needs tailoring to match disparate and varying company data landscapes. The answer lies in being able to maximize direct query competences to your stored data, minimizing the need to move critical data between the consumption systems, or to import your data to external warehouses for analysis.
Some of the design points you must address are:
- Customer experience: Always begin with what your customer needs and work backward. Focus on the different types of users and gain insights into how your operations can tailor your products or services to improve the overall customer experience.
- Infrastructure maintenance: Remove any undifferentiated heavy lifting of managing infrastructure so that you can meet your requirements as and when the demands change and technologies evolve.
- Data source change responsiveness: Any update to the internal services for your data warehouse will need manual updates. This response time is often critical to business decision-makers, making it imperative for you to take a data-driven approach to choose a high-performance architecture.
- Data governance: It is also critical to leverage data platforms to drive financial and data governance within the organization so that you can do ROI analyses, identify spend assets and growth areas – with the objective of reducing the operating costs and gain efficiency.
Aim to deploy a shared responsibility model
As serverless services become more inclusive of our data needs, we are going to see more conventional architectures migrating towards serverless. New ways of ingesting data are becoming commonplace, and this will be a major undertaking in terms of organizational cultural shifts. As such, companies must operate with a ‘shared responsibility model’ wherein both development and operational teams have ownership over their data in the data warehouses.
If you are interested in discussing a solution such as this for your organization, YASH Technologies can help! Our in-house team of experts with specialization across a plethora of domains can help kickstart your data and analytics efforts.