While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.
These challenges start with the data lake architecture itself. While traditional architectures don’t necessarily make it easy to analyze data, we typically recommend an approach where a self-service data lake engine sits on top of cloud object storage like Amazon S3, delivering capabilities like data indexing, transformation, analytics, and visualization that help organizations efficiently manage and analyze their data at scale. AWS data lake best practices show that it’s possible to ingest data in its raw format, optimize costs, as well as query and transform data directly in Amazon S3 buckets.
Let’s take a look at this transformative approach to data analysis, and explore some data optimization techniques to get you on the path to data-driven insights much faster.
If you’ve considered some of the pros and cons of various data management strategies (e.g. data lake vs. data warehouse or data lake vs. data mesh) and settled on a data lake — read on. Data lakes can be the most flexible and powerful solutions, if you know how to approach data storage optimization and analytics.
Let’s look at five of the most effective ways to improve business insights from data, whether you’re looking to improve observability overall or hone in on specific use cases, such as creating a security data lake.
Example of a traditional data lake architecture in AWS.
Effectively optimizing data analytics requires the ability to de-silo your data. That means the ability to ingest any data from any source, then analyze it centrally. Whether you’re dealing with sales records, application logs, security events, or even support tickets, you should be able to move them to a data analytics platform in a consistent way.
The importance of data ingestion processes shouldn’t be overlooked. Ideally, you should be able to ingest data directly, no matter what the data source is. For example, you can take the data produced by your applications (either on-prem or in the cloud) and stream it into cloud object storage (e.g. Amazon S3 or Google Cloud Storage) using cloud services like Amazon CloudWatch or an open source log aggregation tool like Logstash.
READ: Data Transformation & Log Analytics: How to Reduce Costs and Complexity.
Relatedly, the more often you move and transform data in order to analyze it, the more you’ll pay for data analytics. For example, if you’re looking to implement a real-time data lake, the more you move data, the less realistic this strategy becomes.
So strive to keep ingestion, storage and analytics architectures simple. For example, data optimization tools like ChaosSearch enable streaming analytics by transforming existing cloud object data stores into a data lake, giving teams the ability to cost-effectively store and analyze data with multimodal data access, no unnecessary data movement, no complex ETL pipelines, and no limits on data retention.
Data structures come in multiple forms. Some data, like that found in a relational database, is highly structured. Other data is semi-structured: It’s not rigidly organized, but it may at least be tagged or categorized. And still other data has no structure whatsoever.
Your data analytics stack or modular security data lake should be able to accommodate data in all three of these forms -- structured, semi-structured and unstructured -- equally well. And it should not require you to transform or restructure data in order to improve data quality and analyze it.
How the ChaosSearch platform supports a variety of use cases.
Data analytics can support a wide variety of use cases, including business intelligence to power business operations, software deployment and reliability management, deeper application insights for improving customer experience, and beyond.
In order to deliver the greatest value, a data analytics solution should be able to operationalize every potential use case. In other words, you should be able to integrate BI and data visualization tools with a data lake easily and effectively.
Even if you don’t need data analytics for a particular workflow today, who knows what the future will bring. Don’t get tied down by a data analytics stack that can only work with some kinds of data, or that can only deliver insights for certain types of use cases.
Last but not least, be smart about the way you move, transform, and analyze data. Conventional approaches to data analytics essentially adopt a 1970s-era mindset. They rely excessively on indexing, they don’t take full advantage of the cloud, they require inflexible data structures and so on.
For example, Elasticsearch users today are familiar with the problem of having to duplicate index data across multiple availability zones. Duplicating and moving data is meant to provide redundancy in case of outages. If something goes down and you have your data in hot storage, you lose access to it. Not ideal, since the lower tiers of storage are less performant, in the event you need to access or query it.
Let’s say you’re troubleshooting a persistent issue in your system, and you need access to data beyond a 30-day window. Finding the root cause of the problem in Elasticsearch is a major management pain. You never know the exact data you need to access from cold storage or other storage tiers to solve the problem. And because Elasticsearch is built on top of the Lucene search interface, many users experience issues with performance.
To contrast, a modern data analytics approach optimizes processes to improve data quality and performance. It makes full use of compression, avoids unnecessary sharding and leverages high-performance cloud storage to ensure rapid data movement, access and analytics.
To illustrate what data analytics optimization looks like in practice, consider the experience of Equifax.
Equifax needed to support diverse data analysis across multiple internal stakeholders—including a variety of departments and roles such as Customer Service, Security, Compliance, and DevOps—spanning numerous regions and availability zones, all while continuing to surpass the highest security standards.
In particular, managing log data at scale was becoming complex and expensive. Business units were spending money on centrally managed logging solutions—which, in many cases, manifested as siloed stacks within individual organizations’ available cloud services.
The ChaosSearch platform—which activates the data lake for analytics by indexing cloud data—uniquely aligned with Equifax’s needs. ChaosSearch was purpose-built for cost-effective, highly scalable analytics encompassing full text search, SQL and machine learning capabilities in one unified offering. The patented ChaosSearch technology instantly transforms cloud object storage into a hot, analytical data lake.
With ChaosSearch, Equifax finally has a single access pane for analytics across cloud providers and environments. Regardless of the cloud service provider each region chooses to use, ChaosSearch gives users real-time access to business-critical data at scale, without having to compromise on data retention time frames.
Using ChaosSearch, Equifax has minimized hardware maintenance and simplified the management and administration previously associated with log analytics. What’s more, reduced complexity within the analytics process allows teams to innovate faster and rely on long-term data. This helps detect trends in application and infrastructure performance, as well as avoid the risks of persistent security issues. The migration to ChaosSearch has amounted to a 90% cost reduction vs. Equifax’s previous providers.
There are lots of possible approaches to data management and analytics.
Some deliver much better flexibility and performance than others. To make the most of data analytics, you should drive for a solution that is data-agnostic, cost-effective and optimized for speed and fast results.