Big Data technologies such as Hadoop have caused a major disruption in the information management industry. The commotion around it is not only due to 3Vs, Volume, Velocity, and Variety of data but also due to the need to have a single platform for meeting needs across organizational contours. This single or unified platform is called a data lake. The primary objective of a data lake is to ingest data from all known systems within an enterprise and place it in a centralized place to meet enterprise-wide analytical needs.
If the data lake is not properly used, organizations can suffer from costly implications such as limited growth and high overhead costs. A while back, Gartner informed that a multitude of data lake initiatives have failed or are likely to fail, turning data lake into a data swamp.Lack of agility being one of the major reasons. In this blog post, we will delve into the data challenges faced by enterprises, why data lakes have become swamps, explore the attributes of an effective data ingestion solution and how it can help make the data lake more agile.
Data Challenges Faced by Companies
Companies face multiple challenges with data today, from siloed data stores and enormous data growth to costly ingestion platforms. Let us take a look at these challenges individually.
1. Siloed Data Stores
Almost every enterprise is facing issues resulting from siloed data stores that span across many systems and databases. A large number of organizations have multiple database servers. These ecosystems have built distinct data sources for myriad groups like HR, Finance, Supply chain, marketing, and more for their convenience, but they’re having a hard time due to inconsistent results.
2. Enormous Data Growth
Data is witnessing a major growth. In truth, data has grown magnanimously in the last few years, and organizations struggle to manage such massive growth with their traditional databases.
Now, the problem with traditional systems is that they scale vertically and not horizontally. Hence when the existing database reaches its full capacity, additional servers cannot be added leaving companies with a single option: they have to forklift into newer and higher capacity servers. However, even these servers pose limitations. Especially, IT teams have to experience a lot of pressure for handling such huge, complicated systems and data with precision and efficiency. This impacts their productivity, making organizations difficult to work with.
3. Costly Platforms
Old-fashioned databases are essentially appliance-based. Not to mention, the costs posed by these storage systems are exorbitantly high. And, with the surge in the data volume, these costs are only going to increase by leaps and bounds. Plus, due to lower efficiency, data stored in these databases are not secure, and so are susceptible to discrepancies and losses.
4. Poor Business Insights
Using traditional warehouses to store data can affect the quality of insights too. Simply because the aforementioned challenges restrict businesses to stay focused on descriptive analytics and not on predictive and prescriptive analytics for obtaining key insights. When the quality of insights deteriorate, the ability to make accurate decisions see a major decline.
Data Ingestion is the Solution
Data lakes get morphed into unmanageable data swamps when companies try to consolidate myriad data sources into a unified platform called a data lake. To save themselves from this, they need a powerful data ingestion solution, which streamlines data handling mechanisms and deals with the challenges effectively.
Real-time data ingestion solutions, unlike traditional data warehouses that don’t allow businesses to see the data until its curated, enable companies ingest new set of data sources and analyze it within hours and days, instead of months and years.With an effective data ingestion solution in place, enterprises can cleanse and ingest data in real-time, in batches, or using a lambda architecture. Since this process is fast, the risks of succumbing to high costs decrease substantially. As a result, companies can make business decisions easily that save their money in a timely manner. Simply put, companies need an effective, real-time data ingestion solution to make their data lakes more agile and manageable.