Today’s business owners rely on a number of applications and cloud storage units to support their day-to-day data storage activities to support their business goals. This has thus resulted in a constant increase in computer-processing power and storage.
But the problem is with the organizations that lack expertise in data-management approaches. Companies that store data in their traditional warehouses and applications need to find specific employees who are well versed with emerging data-flow technologies, otherwise, this will pose a huge challenge and end up as a setback. While there has been an increase in the numerous tools and technologies currently available to ease the process of data collection and storage of critical business information, many companies are still very unsure how best to categorize the data.
Traditional Data warehouse challenges
Business and IT departments have constantly had their hands full with the sheer volume and variety of data at their disposal, whether it is about customers’ personal profiles, sales data, product specifications, process steps, etc. This data is available in all formats through different sources, hence, every company needs to actively exploit meaningful insights from the overload of business data.
They need to make sure that they do not let data lake management opportunities to simply pass them by. Which is why we are here to suggest a few data tools to simplify the process. To make the most of it, we recommend using these popular open-source Big Data solutions for the organization's data structuring needs
The benefits of the data lake tools are umpteen, and as data volumes continue to expand, companies are increasingly realizing the need for a more agile and unstructured way to manage data.
Qubole is an open data lake platform that provides end-to-end data management services that reduce the time and effort required to run Data pipelines, Streaming Analytics, and Machine Learning workloads on any cloud.
Teradata offers on-premises, in the cloud data intelligence solution. They offer multiple solutions through their platforms such as Vantage -provides unified analytics, data lakes, and data warehouses. Vantage Analyst - Software to simplify data science and advanced analytics for the business analyst and Vantage customer experience – to anticipate customer needs and provide accurate but personalized communications.
Previously called Podium data, Qlik enterprises acquired Podium data in 2018. They essentially help enterprises transform their data resources and offer Data Lake to manage essential features such as organization, transparency, governance, and security. Qlik data’s structure is fast and easy to use, requires no specialized Hadoop skills, and can be easily adapted to suit changing business needs and deliver quick impact.
Snowflake is an analytical data warehouse that enables businesses to store and process varied data in their purpose-built cloud platform. They use semi-structured data to establish deeper relationships with the use of JSON, XML, and Avro, including Parquet data stores, directly in Snowflake.
Many organizations have already started to invest in house data warehouses instead of investing in high tech natives solutions. But it is recommended that they use a structured Data lake architecture and guidance that best fits the organization's data need. In addition, these solutions also provide data lake security and governance, testing, and management of services which are especially useful when the organizations see a peak.
However, it is a time consuming and tedious process to integrate data lakes with other elements of the technology architecture. But evaluating what best fits the needs of the organizations which help realize significant business benefits. Companies need to evaluate data richness, sources, and types and estimate scalability and expandability to ensure that they are optimally spending their business resources.
There is no one size that fits all. Like all other technology projects that organizations undertake, Data Lake projects also have to be thought through in one’s own organization’s context and relevance. It has to be treated like all other business projects and not as a tech solution project. Therefore, it is highly important to evaluate and judge the needs of the organization's data and align the technology accordingly and build options for those specific needs. Else companies will be left with the risk of investing too much time and money but not receive sufficient ROI and business value.