Data Replication

How to Keep Your Data Lake Initiative from Becoming a Data Swamp

Enterprises can utilize data Lakes to increase agile data delivery; however, they can't reap those benefits without addressing the challenges.

The amount of data enterprises capture daily to drive critical business decisions, improve product offerings, and serve customers better is growing at a faster rate than ever before. In 2021, the total amount of data generated in the world was upwards of 74 Zettabytes, with the projection for 2024 being a staggering 149 Zettabytes.  

But what good is all this data if companies aren't able to utilize it to guide insights in a timely manner and in accordance with their immediate goals? 

Enterprises can utilize data Lakes to increase data elasticity and agile data delivery; however, they cannot reap those benefits without addressing the challenges of a Data Lakes initiative. For example, if you try to create analytics-ready data sets from heterogeneous data manually, you'll quickly find yourself in the middle of an extremely complex (if not impossible) and time-consuming project. And when all your data is finally ready for business consumption, it's already outdated. 

What is the Difference Between a Data Lake, Data Mart, or Data Warehouse? 

Before going too in-depth discussing Data Lakes, let’s talk about the differences between a Data Lake, a Data Warehouse, and a Data Mart. While these types of centralized repositories all provide the ability to store data for analysis and reporting, there are some key differences when it comes to structures, data types, and functionality. 

Data Warehouse 

A Data Warehouse is used for companies with a massive amount of data from specific sources, such as an ERP, core systems, or custom applications, and it is usually used for business intelligence, batch reporting, or data visualization. Data Warehouses typically have the following properties: 

  • They represent an abstracted picture of the business organized by subject area. 
  • They are highly transformed and structured.
  • Data is not loaded to a data warehouse until the use for it has been defined.
  • They generally follow a methodology, such as dimensional modeling and textual disambiguation 

Data Mart 

A Data Mart is basically a subset of a Data Warehouse where the data contained is highly accurate for specific users or data consumers. It is subject-oriented and designed to meet the needs of a specific group of users to make tactical decisions for their department. 

Data Lake 

A Data Lake stores raw, free-flowing data, structured or unstructured, from a wide variety of sources, like social media, devices, apps, or productive databases. Their main use is for machine learning, data discovery, or predictive analysis. The data contained within a Data Lake is in a real nature state, not accurate, no improving, just data nature. Some features of Data Lakes include: 

  • All data is loaded from source systems. No data is turned away. 
  • Data is stored at the leaf level in an untransformed or nearly untransformed state. 
  • Data is transformed, and schema is applied to fulfill the analysis needs. 
  • Data lakes retain all data. 

These days, there are several popular solutions in the market for creating your Data Lake. Leading solutions such as Amazon S3, Azure Data Lake, and Snowflake use a cloud-based system architecture.  

How to Start a Data Lake Initiative 

Imagine a scenario where your mission is to move all the information from multiple systems within your company to your new Data Lake system. What is the best way to do this job without impacting your other production systems? 

Using solutions like Syniti Data Replication, you can handle all your connections from multiple source systems to your Data Lake, allowing you to achieve your goals and enable increased efficiency and accuracy without a negative financial impact. 

Typically, to start with a Data Lake project, you have to move your current data from your system through a full load process, or the “Refresh Process,” allowing you to schedule how and when that data loads. For instance, you can schedule to automatically data load in batches overnight, so it’s ready to fresh and up to date at the start of business every day. This thoughtful loading process can help prevent possible network or user issues, making your Data Lake creation as smooth and painless as possible. 

With all your current data stored in your new Data Lake system, you can now take advantage of the functionalities such as Change Data Capture (CDC), capturing only the changed data using log-reading technology. Solutions like Syniti Data Replication deliver a non-invasive and low-impact CDC process for bringing over data from your source systems.  

Accelerate Your Data Lake Initiative 

There are plenty of ways to kick off your Data Lake initiative when you’re ready to learn more. Syniti offers a free trial version with complete functionality, including technical support throughout the entire evaluation process. There’s no risk in learning more about how this ground-breaking solution can help you use the massive amount of data at your fingertips to exact substantial, strategic improvements to your business.

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