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To build a successful big data project, we must know what actionable data it requires and how to analyze and leverage this data to achieve desired outcomes. With the increase in smart applications leveraging artificial intelligence (AI), machine learning (ML) initiatives give insights and drive relevant actions for favorable business outcomes.
Generally, data resides in silos within an organization which has been one of the major roadblocks to companies getting AIML into production. It’s only available in the ecosystem where it’s either generated or operating. To drive business outcomes, we must combine and analyze different data sets to find some correlation. This becomes a challenge with traditional data management approaches.
Data lakes help. They’re a scalable storage repository for data in all formats, whether structured, semi-structured, or unstructured. They store all our enterprise’s data in its raw, native form so we can analyze it and turn it into insights and actions. Data lakes help us acquire, intermix, unify, and converge all data types in one place. This makes them a robust foundation forML and AI applications. We can also leverage data lakes for real-time analysis.
This article looks at data lakes and how to start using one. It examines the differences in ingesting, cataloging, and processing between building a customized data lake and using a black box solution that is offered by a variety of companies. It also highlights some common challenges organizations face when implementing data lakes and discusses dashboard options to get the most out of a data lake.
Getting Started with Data Lakes
Data lakes help eliminate data silos and provide a flexible and agile analytics environment for modern, data-driven organizations. But, instead of throwing all our data into one big pool and dealing with chaos later, we should approach data lakes from a strategic, logical, and business-driven perspective.
To move in this direction, we should have a clear understanding of our goals and use cases. Instead of being enticed — or intimidated — by the buzzword, we must ask:
- How data-driven is our business?
- What goal do we want to achieve with our data?
- How will we get the most value out of all internal and external data?
- Can AI help us achieve our business goals? Can it solve our problem?
- Where do we stand today? What skills do we have, and what skills do we need?
A simple goal of defining a business use case and developing an MVP has the power to massively alter your business, allowing users of all expertise to draw meaningful insights from the data. Whatever long-term objectives we might have for AI — such as profitability, sustainability, customer retention, strategic improvement, and so on — choosing the right platform and architecture is the key to building it. Data lakes usually serve as the basic building block of this data monetizing architecture. While there’s still the option of setting up the data lake on-premises, it can be not only expensive but also requires countless infrastructure experts to tackle the challenges associated with maintaining on -premise hardware. The simpler alternative is to move to the cloud, where AWS offers plenty of cost-effective data lake options.
Custom Build Versus the Buy Approach
AWS provides a comprehensive, secure, and cost-effective set of services for every step of building a production-ready data lake architecture. AWS offers various services whether we choose to make a custom data lake utilizing AWS services that are spun up as infrastructure as code (IAC) or use a third party that has already created the infrastructure and often sells infrastructure as a service.
Building a Customized Data Lake on AWS
A data lake is usually composed of three primary operations, data ingestion, cataloging, and processing. AWS offers a variety of solutions for all data lake operations.
Ingesting
AWS’s data ingestion tools help transfer an organization’s on-premises data to the cloud to implement a data lake. For example, Kinesis Data Streams and Kinesis Data Firehose deliver real-time streaming data, AWS Snowball migrates bulk data, AWS Storage Gateway integrates legacy on-premises data processing, and soon. These tools help build custom applications that allow data analysis and processing using simple SQL queries. If you are migrating from a RDBMS datastore then Data Migration Service allows you to easily move your data into your AWS infrastructure and handle updates, and keeps your on premise store in sync with AWS.
Cataloging
A data catalog is responsible for crucial information about the stored data’s format, classification, tags, and so on. Amazon allows users to access meta data using APIs built on top of the metadata. We can use the Amazon API gate way service to build sites and applications to search the data lake. We can further use these APIs to connect different components like AWS Lambda, EC2, and others to create a catalog.
Processing
AWS provides various highly available and secure data processing services such as Amazon Athena, Redshift, and EMR. On top of that, Amazon also provides some advanced analytics services like KinesisData Analytics and Elasticsearch. These simplify interacting, querying, and analyzing log data. On the top tier is visualization, and ML services likeQuickSight and SageMaker help us make more informed decisions and turn our data analytics into action.
This custom-built architecture fits well with enterprises that have multiple departments needing access to different tools and data. This means we can access what we need for a particular requirement at hand. Though custom data lakes provide maximum control, they do have specific challenges. These include configuration management, data security management, governance, automated environment provisioning, and billing.Without a team with the right expertise, management could quickly become messy, negatively affecting the business.
Data Lake as a Service
Outside vendors data lake solutions automatically configures core AWS services to create a fully-fledged data lake to save us the hassle of managing every piece of infrastructure. This can often simplify tagging, searching, sharing, transforming, analyzing, and governing data subsets, both internally and externally. These services create an abstraction layer that often limits what the customer can do and can cause down stream issues if your use case does not fit into their black box.
If you choose to work with Mission and build out a custom data lake using AWS services we create a CloudFormation template that we can use for configurations.The solution includes AWS services like Amazon S3 for unlimited data storage and management, Amazon Cognito for user Authentication, Amazon Elasticsearch for strong search capabilities and interactive queries, AWS Lambda for microservices, AWS Glue for data transformation, and Amazon Athena for analytics.
This data lake architecture leverages security, scalability, and durability and enables all users to easily access organizational data according to their role and the organization’s policies. Users can also manipulate and analyze that data. AWS Management Console provides complete data security and encryption as per organizational policies. Users can search and browse datasets using their attributes and tags from the solution console. It enables automatic provisioning and de-provisioning of the environment as well.
If you are hoping for a fully dedicated and managed data lake, thenMission is able to support you with our Managed Data Ops offering that utilizes the cloudformation scripts developed by the team during the initial build out or MVP phase. These templates enable us to spin up a secure data lake in a few clicks as you go into production. AWS automatically collects, catalogs, and transfers data to the Amazon S3 data lake bucket. It cleans it, transforms it, and provides insights using machine learning algorithms. All we need is to define where our data resides and our policies. AWS takes care of everything else.
This architecture’s simplicity and Missions fully managed services are best suited for organizations that want to avoid low-level system configurations and instead focus on innovation and building their core business.
Leveraging Dashboards
End users usually only care about the tip of the iceberg — data visualization. Generally, they pay little to no attention to what’s going on beneath the surface. We’ve already established how easy AWS makes it for engineers to set up and manage a data lake. AWS also enables integrating the data lake with other AWS services. For data visualization, it’s easy to feedAWS services like Amazon QuickSight with data from Amazon Athena or Redshift.
We can run standard SQL queries to analyze data in our data lake using fully managed, cost-effective, and interactive query services like AmazonAthena and Redshift. Then, we can feed this data into QuickSight to analyze the information visually. We can even create and share interactive dashboards for all our organization’s users without configuring or managing any hardware.Running dashboards on top of our data lake enables us to quickly analyze vast amounts of data and make intelligent decisions for positive business outcomes.
Challenges and Pitfalls
Though data lake technology is rapidly growing, it hasn’t matured yet.This creates implementation challenges. Also, data investment is costly, so it’s best not to make any impulsive decisions. Let’s explore some common data lake challenges.
Identifying a Use Case
At the beginning of this article, we mentioned that data lake decisions should always be business and logic-driven. It’s essential to understand business problems clearly and know if we have the data to solve them before starting our data journey.
Organizational Challenges
The whole point of being a data-driven organization is to generate favorable ROI for the business. Unless there’s a sizable ROI, it can be challenging to get executives to approve a significant investment like implementing a data lake. Analyze the numbers to ensure this approach makes sense before moving forward.
Build Versus Buy
Although various providers offer data lake services, there’s a lot to sort through and navigate. Should we opt for an on-premises or a cloud-based data lake? Which technology is best suited for our use case? Should we build or buy the whole stack? Is the data reliable?
There are also additional challenges in creating a data lake, including quality enforcement and governance.
Conclusion
In this article, we outlined the benefits and key challenges of implementing a data lake. Most organizations are already sitting on datasets so massive that humans can’t manage and analyze them unaided. Data lakes provide easy storage for all of the organization’s data and power the AI and ML solutions for more informed and profitable decisions.
But a data lake is a complex solution with many layers and requires many tools and technologies to achieve its full potential. Whether you choose to build from scratch or buy a solution, your data lake requires careful consideration and effort in deployment, maintenance, and governance. Moreover, we barely scratched the surface of AI services offered by AWS to leverage data lake. When these AI services provide an inspiring opportunity for a better return, choosing between them could also be a daunting challenge.
Consider consulting with domain experts to help you start on your data journey. Mission, for example, provides consultation and managed cloud services for AWS. Mission‘s expertise helps analyze your business use cases and determine how to leverage your organization’s data to get the most value. Explore Mission’s successful use cases or get in touch to learn more.
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