5 Ways to solve the disparate data problem and drive business outcomes

Enterprise data architectures are not pristine. They evolve to create a patchwork of different data systems, structures, and formats. Somehow, engineers must stitch everything together to make the company’s business data useful.

This disparate data problem undermines strategic and operational decision-making. Data silos make essential data inaccessible. Data lakes become swamps of stale information. Data warehouses freeze analytics in pre-determined structures.

An Icehouse architecture solves this problem by leveraging open-source technologies and federating disparate data sources. Adding the right tools and new data management approaches to your existing infrastructure lets you generate insights from any data source and accelerate effective decision-making. 

What is disparate data?

Disparate data is the problem created by fragmented, poorly connected data resources. Aggregating disparate sources scattered across organizational, technological, regional, and regulatory boundaries is expensive and time-consuming. As a result, business leaders must base decisions on incomplete, stale, and low-quality data.

Let’s look at five ways a Starburst can overcome the disparate data problem.

1. Use the right tool for the job

A data repository serving as a single source of truth ought to solve the disparate data problem. Despite decades of effort, success remains elusive. The rise of big data only made things worse. There is a better way: the Icehouse architecture.

A single source of truth’s performance and cost constraints

Logically, merging datasets from relational databases, real-time systems, and other data sources into a single source of truth would simplify analytics and decision-making. In practice, not so much. 

Developing and maintaining pipelines to ingest data from separate systems requires significant investments in time and resources.

In addition, proprietary data warehouse solutions are expensive and difficult to scale. They couple storage with compute so any attempt to optimize for one requires over-investments in the other.

Data lakes promised to solve this problem using affordable cloud object storage systems. However, they could not match a warehouse’s analytics performance and data management tools.

Icehouses unify disparate data sources

An Icehouse architecture uses a fundamentally different design philosophy to avoid these challenges. Rather than bringing all data into a single location, the Icehouse federates disparate systems to unify access within a single consumption layer. 

This federated approach decouples storage from compute, allowing data teams to optimize their storage strategies while using dynamic cloud computing to process petabyte-scale datasets on demand.

Starburst’s Icehouse data analytics solution has connectors to over fifty enterprise data sources, from transactional databases to real-time streaming systems, letting companies create a single point of access to data across the organization. 

2. Disparate data sources limit the copying and moving of data

Data motion drives many challenges associated with a centralized repository, from constraints on engineering resources to network congestion to compliance risk.  In contrast, a Starburst-powered Icehouse architecture leaves data in place.

ETL pipelines generate overhead and risks

In addition to monopolizing engineering time, ETL data integration pipelines require excessive infrastructure investments. Companies need extra storage since the extraction phase lands petabytes of raw data into a staging area where transformations can execute safely. Transforming the data consumes enough computing resources to impact operational systems, as does the movement of such large datasets across networks. Data teams must run these processes in off-hours batches to prevent these impacts.

This batch processing also increases business risk since the same data can reside at the source, in the staging area, and in the warehouse. This redundancy expands a company’s attack surface, giving hackers more opportunities to exploit a security breach. 

Data ingestion also creates operational risks should issues like resource constraints cause batch-processing workflows to fail. Scheduling another run could take hours or days, making the final dataset less useful to decision-makers.

Data consumption layer queries data where it lives

Much of the raw data flowing into an ETL pipeline’s staging area can be irrelevant, getting thrown away during transformation. A Starburst Icehouse architecture queries data where it lives through pushdown, dynamic filtering, and other features. Only the final results flow back across the network, reducing resource utilization. Additionally, leaving data at the source also lessens the risks of data redundancy, as there’s no need for staging areas.

The Icehouse architecture is less dependent on ingestion pipelines but still makes batch processing more reliable. Starburst’s fault-tolerant execution seamlessly recovers from minor errors to ensure timely pipeline completion.

3. How should you organize your data? Leave it in open formats

Proprietary data systems have always come with high costs and limited scalability, while vendor lock-in practices made moving to a better option too difficult. Icehouse architectures are based on open-source technologies that give companies control over their data.

Organizing data with open table formats

An Icehouse uses the Apache Iceberg open table format to organize data and enhance query performance. First developed at Netflix to address the limitations of Hive and the Hadoop ecosystem, Iceberg combines snapshot isolation, partitioning, and schema evolution with full ACID compliance to support performant, scalable data analytics.

Access data with open query engines

Iceberg was developed to work with the Trino open-source SQL query engine. Trino uses massively parallel processing to deliver low-latency results efficiently at scale.

Starburst is the enterprise implementation of Trino, founded by the open source project’s creators. Available as a self-managed distribution or a cloud-based, fully-managed solution, Starburst provides performance and security enhancements to Trino’s already performant query engine.

Starburst and Trino also help democratize data access to enable data-driven business cultures. Trino’s use of ANSI-standard SQL makes data accessible to anyone with the right tools. Data scientists and engineers can use the Trino API in their code. Analysts can run complex queries from their preferred business intelligence and visualization tools. Even the least technical users can easily get the data they need through user-friendly data products. 

4. Leverage data products

Data products are purpose-built, reusable datasets designed to solve targeted business problems. Often aligned with KPI metrics, data products deliver the right information in the right format so decision-makers can act faster and with more confidence than legacy data analytics systems allow.

Rigid data warehouse analytics constrains decision-making

The highly structured nature of data warehouses constrains how companies incorporate data into decision-making processes. That structure makes data useful to the broadest range of users which means it isn’t optimized for anyone. Data marts are one way to address this challenge. Essentially mini-warehouses optimized for particular use cases, data marts require an additional layer of ETL pipelines and ongoing data team support. The data management requirements will limit data mart development to the few use cases that justify the costs.

The range of data insights available from a warehouse is likewise limited. Ingested data has already been transformed, erasing any insights that could be gained by looking at raw data in a different way. Getting those insights requires developing a fresh ETL pipeline and allocation of storage infrastructure.

Data products accelerate time to insight

Unlike engineering-driven warehouse analytics, data products are business-driven and are best developed on an Icehouse architecture. Data analysts and domain experts use the SQL they already know to process the data they need and deliver it in a format their decision-makers will understand. A common use case is to create aggregate data products that align with corporate KPIs. As business priorities change, domain experts can easily evolve the data products accordingly.

As data scientists build data products based on machine learning and artificial intelligence, they can use Starburst’s discovery resources to explore datasets before finalizing their ingestion workflows. Using ETL pipelines for this iterative process is time-consuming and expensive. Using the self-serve capabilities of an Icehouse enhanced with Starburst’s search and discovery resources streamlines algorithm development.

5. Leverage your existing investments

Previous attempts to solve the disparate data problem required new information technology investments. Data warehouses replaced databases and were, in turn, replaced by data lakes. But the truth is, nothing was ever replaced. Trying to create a single source of truth merely added more sources.

An Icehouse architecture does not try to replace your legacy solutions with yet another technology. Instead, it builds upon and unifies your existing investments.

Leave data where it is

As discussed earlier, Starburst’s connectors link different data sources into a holistic access layer. There’s no need to migrate data into another repository. It can stay where it lives. From the user’s perspective, the data’s physical location becomes irrelevant since they always access it through Starburst.

Optimize your data lakes

One reason data lakes became data swamps is because they tried to do too much. Just because a data lake could store data from every potential source doesn’t mean it should. A Starburst Icehouse lets data teams optimize their data lakes. Rarely used data can remain at the source, always retrievable with a single SQL query. Data collection can then focus on populating the lake with the most frequently accessed data to optimize latency and operational efficiency.