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Data swamps filled with unstructured data, undocumented, and increasingly inaccurate data become expensive data assets that are too difficult and expensive to use.
Fortunately, you don’t have to stay mired in the swamp. Here’s what you need to know about data swamps, how they quickly corrupt data lakes, and what you can do to clean things up
A data swamp is the inevitable outcome of a company’s misunderstanding of how data lakes work. Without a clear and well-supported big data strategy, lakes quickly fill with old, irrelevant, undocumented, and inaccurate data. Analysis quickly bogs down as data becomes less discoverable and more unreliable.
Data swamps result from poor design, weak governance, and inadequate maintenance. Here are 8 signs that your data lake is becoming a swamp:
A poorly thought-out data lake strategy is at the core of any data swamp. Too often, companies frustrated with the inflexibility and cost of their data warehouses turn to the promise of a data lake’s flexible, low-cost unstructured data storage.
What they forget is that with great flexibility comes great responsibility. A data lake requires intentional and careful planning to fulfill its promise. Where the data lake sources raw data and how data teams maintain their lake determines how efficiently the data customers can analyze that data.
Data swamps are inevitable without a data lake strategy and the resources to go with it.
While a data lake’s strength is its ability to hold many types of data, swamps become dumping grounds for unstructured, semi-structured, and structured data. Haphazard ingestion approaches result in format inconsistency from data source to data source.
This variation imposes significant challenges on integration and analysis. Business intelligence analysts can only pull data together with help from the data team. Engineers must devote substantial time to processing each data source to make formats consistent.
Data lakes require constant care and feeding to maintain data quality. Freshly ingested data may be incomplete, inconsistent, or inaccurate. Aging data becomes less accurate and relevant.
In the absence of a strong data lake strategy, data teams rarely get the resources they need to keep a lake’s data clean. The resulting swamp quickly fills with dirty, low-quality data that requires considerable effort to turn into insights.
A misguided belief that data lakes will turn a company into a data-driven operation leads to just-in-case accumulation of data with no relevance to the company’s strategic goals.
A lake filled with irrelevant data becomes a swamp that contributes little to decision-making. Moreover, it becomes an expensive swamp since the company must pay for the storage of data it never truly needed.
Data lakes deliver on their promise by becoming the organization’s single source of truth. Continuous data curation is the only way to prevent data duplication as lakes ingest data from more sources and as existing data ages.
Swamps get clogged with redundant data, exacerbating storage costs. Data redundancy makes analysis more difficult as engineers, data scientists, and analysts struggle to identify the source of truth among an array of alternatives.
Weak data management practices result in lakes that store data without documenting the data’s lineage or provenance. This lack of traceability weakens insights and increases risks.
Without clear, traceable data lineages, analysts can never know whether or not the data emerging from the swamp is reliable. This added uncertainty directly affects analysts’ ability to produce clear insights and leaves an element of doubt in resulting decisions.
Adding to corporate risks, a data swamp’s limited traceability impedes data governance and compromises compliance. If auditors cannot determine the provenance and lineage of data, the company could be subject to regulatory action.
A data lake is supposed to foster better decision-making by placing all the data at the company’s fingertips. Swamps are murky at best. Discovering data or locating specific data sets becomes almost impossible.
When a data lake works, it accelerates speed to insight by giving analysts quick access to the data they need. Swamps are too disorganized and full of low-quality data.
Analysts can’t begin until after data engineers build pipelines to extract and clean the data.
Even then, lots of data is of such uncertain quality and lineage that errors easily creep into the analysis.
Data swamps are everything data lakes are not.
Lakes are carefully planned, well-managed data repositories subject to consistent governance standards that ensure the company makes effective decisions based on high-quality analysis.
By contrast, swamps are expensive storage dumps of inconsistent, duplicative, inaccurate, and undocumented data that are challenging to discover, collect, and analyze.
Unfortunately, the nature of data lakes makes them susceptible to becoming swamps. Every new data source requires more extract, transform, and load (ETL) pipelines to make the ingested data ready to deliver value.
This slow, costly process is fragile and requires constant maintenance, which limits the resources available to manage the data lake. Here are 5 ways data lake challenges can yield data swamps:
Data lakes require unambiguous and consistent policies for ingestion, quality control, and organization. Without clear data governance, data quality degrades while irrelevant and duplicative data proliferate. The lake becomes more opaque as it becomes a swamp.
Data engineers and analysts need to understand what data the lake offers to include the right data in their work. Data discoverability depends on strong, well-documented metadata and the quality of a lake’s data catalogs.
If a lake’s metadata is not constantly captured, cataloged, and updated, the data disappears into a swamp. Self-service analysis becomes impossible. And over-burdened data teams see their workloads increase.
It’s better to ask “what data should we ingest” rather than “what data can we ingest” when feeding a data lake. Companies that prioritize big data that supports actionable insights will foster thriving data lakes. Companies that collect all the data they can, just in case they might need it some time for some reason, will pay a lot of money for a swamp that contributes little to strategic goals.
The history of enterprise data architecture is one of constant re-engineering as a new generation of proprietary systems displaces the old. Without open formats such as ORCFile, Parquet, or Avro, vendor lock-in limits your architecture options.
Data growth continues to accelerate, adding to the data management burdens that can turn lakes into swamps. Whether managed on the company’s infrastructure or provisioned in the cloud, data lake architectures do not support rapid scalability.
A data lake’s development and long-term maintenance are technically complex and expensive. Companies that don’t recognize this fact fail to form data lake strategies.
As a result, data teams struggle to manage lakes in ways that preserve data quality and governance. Cleaning up a data swamp — or keeping a data lake pristine — requires a new approach.
Our fully-managed platform separates data storage from compute so consumers can access data from any source within a single, easy-to-use interface.
Improved data analytics and decision-making are only two of Starburst Galaxy’s benefits. Data teams can leverage the platform’s governance, data discoverability, and usability features to clean their existing data swamps while introducing long-term strategies to manage their data lakes efficiently.
Starburst includes everything you need to install and run Trino on a single machine, a cluster of machines, or even your laptop.
Up to $500 in usage credits included