The Importance of Data Lakes and What They Mean to the Explosive Growth of Data

The Data Lake is Where the Business Will Find the Biggest Nuggets of Insights Needed to Transform the Business

It sounds appealing – easily store business data in a single location, where all of your departments and applications can access it and put it to use. It’s no wonder that the interest in data lakes continues to rise rapidly as the importance of data is exploding. Data lakes are the natural place to store the influx of data businesses were collecting, especially when business leaders might not know exactly what they will be using the data for.

A data lake is a central location that holds a large amount of data in its native, raw format without the need to impose a schema (a formal structure for how the data is organized). Compared to a hierarchical data warehouse, which stores data in a structured, row and column format. By leveraging inexpensive object storage and open formats, data lakes enable businesses to better access and operationalize their data.

But What is a Data Lake?

Data lakes are able to store a large amount of data at a relatively low cost, making them an ideal solution to house a business's historical data. A data lake offers companies a more cost-effective storage options than other systems because of the simplicity and scalability of its function. Because data lakes keep all data in its raw format, businesses can send the data through ETL (Extract, Transform, Load) pipelines at a later stage once you know what queries you want to run. Storing data within a data lake enables businesses to store data without prematurely stripping it of vital information.

When businesses store data in individual databases, they unknowingly create data silos. Data lakes remove those silos and give access to historical data analysis so every department can understand customers more deeply with the same data. Businesses that combine all their data into a data lake can set themselves up to take advantage of a host of benefits:

  • Power Data Science and Machine Learning

Data lakes allow businesses to transform raw data into structured data that is ready for analytics, data science, and machine learning with low latency. Raw data can be retained indefinitely at low cost for future use in machine learning and analytics.

  • Centralize, Consolidate, and Catalog Your Data

A centralized data lake eliminates problems with data silos like data duplication, multiple security policies, and difficulty with collaboration. This provides downstream users with a single place to look for all sources of data.

  • Quickly and Seamlessly Integrate Diverse Data Sources and Formats

All data types can be collected and retained indefinitely in a data lake, including batch and streaming data, video, image, binary files, and more. And since the data lake provides a landing zone for new data, it is always up to date.

  • Democratize Your Data by Offering Users Self-Service Tools

Data lakes are incredibly flexible, enabling users with completely different skills, tools, and languages to perform different analytics tasks all at once.

Data Lake Challenges

Despite their benefits, many of the promises of the data lakes have not been realized due to the lack of some critical features: no support for transactions, no enforcement of data quality or governance, and poor performance optimizations. As a result, most of the data lakes in the enterprise have become data swamps.

  • High Cost

Data lakes can be very expensive to implement and maintain. Although some data lake platforms are open source and free of cost if business IT teams build and manage the data lakes themselves, doing so often takes months and requires expert staff

  • Long Time-to-Value

Even after businesses spend months setting up their data lake, it will often be years before it grows large enough and becomes sufficiently well integrated with their data analytics tools and workflows to deliver real value.

  • Management Difficulty

Even for skilled engineers, data lakes are hard to manage. Data lakes can store large amounts of unstructured data, therefore, businesses need to have good data management practices otherwise their data lake may turn into an unusable data swamp.

Data Lakes and the Importance of Architecture

Synatic allows businesses to create a data lake that serves as a central repository of information, right inside Synatic. Data can be collected from multiple sources and moved into the Synatic data lake in its original format. The data stored in Synatic’s built-in data lake can be used to build reports by publishing the data into a BI tool where the data would automatically be analyzed and the insight used to drive decision making. What’s more, Synatic’s error management capabilities allow businesses to catch duplicate data and ensure reports remain duplicate free. Businesses need to use a broad range of tools for implementing rapid ingestion of raw data into the data lake. Synatic’s  Hybrid Integration Platform (HIP) has a range of tools that businesses need to build a high performing data lake with incomparable speed. From Extract-Transform-Load (ETL) and Integration to API Management tools, Synatic can answer any business's data lake requirements with a single platform.

Synatic lowers the total cost, dramatically improves time to value, and simplifies management of your data lakes. The data automation solution helps you unlock the full potential of your data by providing ready-to-use data lake capabilities. Unlike traditional data lake solutions, Synatic’s HIP takes care of your data lake needs, so you don’t have to. If you want to learn how to lower data storage costs and save your engineering team time building and managing your data lake, contact Synatic today.

Max P. Smith
June 28, 2022
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