5 Best Practices of Effective Data Lake Ingestion
Having a data lake can a be a boon to your organization, but only if you do it right. In this article, we go over the basics.
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In the world of the continuously fluctuating technology landscape and customer-market policies, data has become one of the biggest business assets. It reinforces and sharpens an organization’s ability to gain a lead over the competition. Thus, it is a key value-creator, and its management, regular maintenance, and storage have become important for businesses planning their continued success in the future. The technology advances over the years have been advantageous in terms of data creation and storage, still, they are never sufficient for efficient data management. At times, businesses struggle to take advantage of voluminous information to their benefit. This is where a data lake can help.
The data lake is allowing businesses to hold, manage, and exploit disparate data, structured and unstructured, data, and external and internal data to their benefit. But here’s the reality — some data lakes fail to serve their purpose due to their complexity. This complexity may be induced by several factors, among them improper data ingestion. Building a sound data ingestion strategy is one of the keys to succeed with your enterprise data lakes. This post outlines the best practices of effective data lake ingestion.
Address Your Major Business Problems: Are there businesses who build data lakes just for the sake of it? Yes, there are many. Those who build data lakes to address their business problems are likely to succeed over non-planners. This may seem like a basic tip, but there are IT teams who might be seriously considering transforming their data lakes into science projects. They think it will serve their purpose in the future, which is not true. It is important to stay committed to a problem and find its answer — and if building a data lake is the right way to go, then great!
Automate Data Ingestion: Typically, data ingestion involves three steps — data extraction, data transformation, and data loading. As data volume grows, the three-step process becomes complex, and takes more time to complete. Earlier, data ingestion was manually accomplished, however, nowadays it is automated. This is because companies rely on several digital sources, and data comes in 24/7 in various formats. It is a laborious task for companies to convert incoming data into a single, standardized format manually. This is why more companies are resorting to various automated data ingestion tools to ingest data efficiently. Many enterprises use third-party data ingestion tools or their own programs for automating data lake ingestion. These tools assure success up to a certain extent, however, they cannot conduct root cause analysis on their own, in the case of failure. Hence, it is important to choose a platform that not only automates data ingestion into the data lake, but also conducts other tasks such as quality checks of incoming data, managing the data lifecycle, and automating metadata application, thereby helping your team to identify the root cause analysis.
Choose an Agile Data Ingestion Platform: Again, think, why have you built a data lake? You want to ingest, store, manage, and access a huge volume of data that has come your way, right? If you realize this, it is be easy to design a data ingestion process that can handle any volume of data. Always take care to choose an agile data ingestion platform that is elastic and scalable, that can also survive the spikes in data volume at times. Additionally, developing soundproof data retention strategies such as where data will be stored and how long will it be stored, etc, will help you in a long run.
Utilize the Benefits of Streaming Data: If you are yet to consider streaming data as the main information source then there is a chance that you are missing out on a key element of the data revolution. In many industries, streaming data is an important aspect of their business model. For businesses following a Business to Customer (B2C) model, data streaming helps analyze customer behavior. Hence, while designing a data ingestion strategy for your data lake, it is important to think of different types of data you may receive, including streaming data, files, or batches of data coming from different sources.
Set Notifications: As discussed above, data ingestion starts with a series of coordinated processes. A notification needs to be written to inform the various applications for publishing data in a data lake, and to control or trigger their actions. For instance, a sales application may issue a request for data in a certain format including the client name, the status of the sale, price, and will receive a notification, once the data in prescribed format is available. This streamlined application scheduling will help gain a better control over the data lake, and improves transparency and traceability.
Data ingestion in a data lake is a process that requires a high level of planning, strategy building, and qualified resources. Overall, it is a key factor in the success of your data strategy. By devising the right data ingestion strategy, and utilizing the right set of data ingestion tools you are on the right path of creating a productive data lake.
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