ChaosSearch is participating in the upcoming Gartner Data & Analytics Summit (May 4-6), a virtual conference for professionals and executive leaders in Data & Analytics (D&A). The summit will feature expert talks from Gartner analysts, engaging workshops, and the opportunity to participate in roundtable discussions with D&A professionals and executive leaders.
This blog post was inspired by the tagline of this year’s Gartner Data & Analytics Summit: Learn, Unlearn, Relearn. To keep pace with change, cope with uncertainty, and drive value creation with Data & Analytics, organizations must be willing to learn new skills and capabilities, unlearn obsolete or outdated methods, and continuously relearn what works.
Building on this theme, our blog post this week looks at the key forces driving change in enterprise data lake architecture, the limitations of current enterprise data solutions, and the innovative technologies behind our modern approach to enterprise data and analytics.
Change has been a constant throughout the evolution of Big Data & Analytics.
The emergence of the Internet in the early 2000s allowed companies to collect more data than ever before, but growing data volumes in siloed relational databases made the data difficult to access, expensive to store, and slow to analyze. Then change happened: data warehouses were created and organizations could now store all of their data in a single centralized location.
When datasets became too large to be processed by a single computer, it was time for another big change: Hadoop was released in 2006, enabling distributed processing of datasets across multiple computers.
Over the next five years and beyond, continuous growth in the volume, variety, and velocity of enterprise data would drive another major change: the adoption of the cloud computing model, which allowed organizations to minimize their data storage costs by migrating data warehouses into the cloud.
With the public cloud in place as a reliable data storage solution, change accelerated in other areas: data indexing and querying solutions, data transformation techniques, visualization tools, and advanced analytics technologies like AI and machine learning.
Learn, Unlearn, Relearn is all about embracing the rapid change in Big Data & Analytics. It’s about challenging the status quo and finding news ways to leverage enterprise data into business value. Over the past two decades, the most successful enterprise organizations have embraced change by updating their enterprise data architectures to leverage new technologies, accelerate time-to-insights, make better decisions, and ultimately drive value creation.
With that in mind, let’s take a closer look at what enterprise data experts today can learn, unlearn, and relearn to transform their enterprise data architecture and create a modern data strategy that leads their organizations into the future of Data & Analytics.
Business leaders seeking to transform data and analytics within their organizations today should first understand the major challenges and key forces that are driving change and innovation in enterprise data architecture. Below, we identify two key factors at play in 2021 and their implications for the future of enterprise data & analytics.
Large organizations in 2021 depend on a growing number of applications to run their daily operations. As a result, they are generating and collecting more data than ever before, faster than ever before, and in a great diversity of structured, semi-structured, and unstructured formats.
While data storage is no longer a major stumbling block, the growth of big data is driving the development of new data indexing, cleaning, and analysis tools that can speed up the insight generation process and make it easier for organizations to draw insights from ever-expanding data streams.
In 2021, we’re seeing a sharp uptick in new regulations surrounding data security, privacy, and sovereignty around the world. These policies create new challenges and data governance requirements for enterprise orgs that do business internationally. To maintain compliance, organizations are adapting their data architectures to ensure centralized control and governance of all organizational data.
The ability to transform data into insights, and insights into action, is a competitive advantage for the modern, data-driven organization. Accelerating time to insights requires the adoption of enterprise data architectures and technologies that streamline the data life cycle and reduce latency between data creation and analysis.
Unlearning is a process of understanding which Data & Analytics best practices, technologies, processes, and working methods are ripe for change and improvement. Here’s our take on the failings of current enterprise data architectures and why they’re no longer meeting our needs in a Big Data World.
Data warehouses follow a schema-on-write approach, meaning that the data must have a defined schema before writing into the database. As a result, all warehouse data has been previously cleaned and transformed, usually via some iteration of an ETL process. When business intelligence teams access the data warehouse, they’re accessing processed data - not raw data.
The problem here is that analyst teams are only exposed to data that’s been transformed in a specific way to support predetermined use cases. The lack of access to raw enterprise data limits innovation and prevents BI teams from transforming data in different ways to reveal new insights or uncover new use cases.
In the ETL process, data is captured from transactional databases and other sources, transformed in a staging area, then loaded into an online data/analytics warehouse where business intelligence (BI) teams can run queries.
But as organizational data assets continue to grow at 30-40% per year, the ETL process is not getting 40% faster. This often leaves enterprise data teams with a tough choice: reduce data utilization to speed up processing times, or accept the increase in time-to-insights.
Enterprise organizations are now using solutions like Elasticsearch and ELK Stack to index their data, making it searchable and supporting analytics and BI use cases. These solutions use the Lucene database storage format which does a good job of supporting fast analytics but comes with a significant shortfall: Lucene indices can become extremely large, up to 2-5x the size of the data source, resulting in degraded performance along with increased costs and complexity.
Organizations still need a fast querying approach, but there’s a clear need for a new approach to Data Indexing that compresses the source data rather than expanding it to unmanageable proportions.
Read: 5 ELK Stack Pros and Cons
As innovators like ChaosSearch continue to push boundaries in data and analytics technology, business leaders will need to reimagine their enterprise data architectures to keep pace with the competition. They’ll also need to relearn the best practices, techniques, and working methods they’ve adopted for making the most of their data.
Here’s how our powerful new approach to cloud log analysis is inspiring data leaders to upgrade their enterprise data architectures.
Indexing supports analytics use cases by making your data searchable, but current solutions don’t perform well at scale.
That’s why we created Chaos Index®, a proprietary data format that delivers auto-normalization and transformation, supports text search and relational queries, and can index all data from any source with up to 95% compression.
The ability to fully index raw data with high compression means that enterprise organizations use less storage, network, and compute resources to support their analytics needs. Chaos Index also performs well at scale, so data architects can achieve rapid time-to-insights, even with large data sets.
We’ve already highlighted the shortfalls of the ETL process, which eats up more time and resources as enterprise data continues to expand in its size and scope.
Our solution to the ETL process is Chaos Refinery®, an in-app tool that allows our users to clean, prepare, and transform indexed data directly in ChaosSearch with no data movement. For most enterprise organizations, the largest delays in the data pipeline happen because of data movement and the ETL process. With the ability to index and transform data directly in Amazon S3 buckets with ChaosSearch, enterprises can eliminate those delays, accelerate their time-to-insights, and Know Better® than the competition.
By creating better ways for enterprise organizations to index and transform their data, we’re advancing our large-scale vision for enterprise data architectures and helping our users achieve the true promise of data lake economics for their organizations.
Here’s how it works:
You capture enterprise data of all types and from every source and store it in Amazon S3, leveraging economies of scale to get the lowest possible storage costs.
Once your data is in Amazon S3 buckets, it can be indexed by ChaosSearch to enable full-text search and relational querying.
Then, using the in-app Chaos Refinery tool, you’ll be able to clean, prepare, and transform your data at query-time using a schema-on-read approach with no data movement.
Finally, you can use the integrated Kibana Open Distro tool to easily create visualizations and build dashboards to support analytics use cases.
The end result is a powerful data lake repository that actually functions as a data lake. Data is stored in its raw format at a low cost where it can be accessed, transformed, and analyzed on-demand by anyone in your organization. With no ETL process and our schema-on-read approach, data warehouses are a thing of the past. Instead, our Data Platform accelerates time-to-insights while lowering costs and removing complexity from the data pipeline.
Read: 10 AWS Data Lake Best Practices
At the upcoming Gartner Data & Analytics Summit (May 4-6), we’ll be presenting a live demo of ChaosSearch, our innovative Data Platform that empowers organizations to modernize their enterprise data architecture, achieving the true promises of data lake economics and data democratization. We hope you’ll join us to learn more about our vision for the future of data & analytics.