The data landscape for almost all industries is undergoing a renovation. The volume of enterprise data, both internal and external, is increasing exponentially. This dramatic change is forcing many companies to believe that their traditional data warehouses are no more capable of handling the diversity, enormity, and speed of data. Many companies are shifting towards data lakes to manage the humongous data, find new revenue streams, and reduce cost.

Data lakes can be established on clouds (more preferred due to agility and flexibility) or on Hadoop to store raw data in large quantities and from varied sources. The capability of data lakes of handling diverse data in large volumes and from heterogeneous sources present them as a promising substitute to data warehouses.

While more than 30% of data is often dumped in legacy systems, nearly 40% of it goes unused due to large volumes. This has encouraged many companies to build data lakes on clouds from scratch. However, using a data lake to complement a data warehouse can result in a drastic enhancement in database management and data analytics and can provide unparalleled real-time insight for data-driven decision-making.

Glorious Insight, one of the leading providers of BI and data analytics solutions, exploits the combination of data warehouse and data lake to accomplish high-speed data processing and deliver products that are flexible and scalable to accommodate business growth. Our systems can efficiently capture structured, unstructured, semi-structured, and stream data.

This helps in deriving deep insights in real-time and making more informed decisions on the fly. We create a customized rich data analytics ecosystem for your company to handle the growing amount of data for making useful and profitable conclusions and action plans.

√  Amalgamation strategy

Nonetheless, it can be implemented over your traditional technologies and system, it is important to understand that data lake is a new technology and not any other database. Based on your business objectives and your expectations from the system implemented with a combination of data warehouse and data lake, creating the combination can be a demanding task.

You need to make a clear distinction between creating a new environment that is apt for a particular job or modifying your existing system to fit in the task by forcing your employees to put in more time and effort.

The pandemic has caused a dramatic change in the pace of industrial automation. Several domain experts predict that the spending on RPA by various companies is likely to go up by several hundred percents by 2025. The RPA implementation in medical and health care has helped a large number of companies in data collection, generating health alerts, and obtaining government approval for contact tracing, stimulus assistance, etc, and ensuring business continuity.

For instance, you can use the same method and software to import data into a warehouse and a lake. But the ETL (Extraction, Transformation, loading) process is different. When a data lake is established over a traditional system, ETL is performed outside the Hadoop cluster. The process is easier, faster, and cheaper with data lakes and frees your employees for innovation and analytics. A similar difference is in the security requirements of the two technologies.

By working closely with your team and gaining a comprehensive insight into your systems, Glorious Insight can formulate effective strategies to ensure success. We create efficient architecture and pick the hardware and software wisely to meet your companies requirements. As the data in the data lake is ingested irrespective of its completeness and quality, we deliver database management systems that apply metadata to provide quality to the data. We pay special attention to the following complex tasks.

•  Data governance

Our systems allow you to define rules for data governance and apply them to manage, track, access, and protect data throughout the pipeline.

 • Automation

Automation is necessary to operate data at a big data level as the lake size grows. Functions such as data ingestion, data lifecycle management, and metadata management, etc are automated.

 • Metadata

Metadata helps you find useful and meaningful data from the data lake, apply data governance rules, and use it with confidence.

•  Business Intelligence and visualization

With a data lake, you need business intelligence tools that can perform with real-time data with no latency. It should be able to handle stream data and generate easy-to-understand and attractive visualizations.

•  Leveraging clouds

To draw the maximum benefits of the data lake ecosystem, shifting to cloud architecture is a smart move. It allows the highest level of flexibility and scalability to accommodate your present and future business needs. Experts suggest that the cloud-based data warehouse solutions would see a promising CAGR of 12 to 15%.

√  Industry transformation

Glorious insights have worked with several industries for the successful implementation of enterprise data warehouse augmentation. We have created database architecture to deliver the maximum benefits of conventional data warehouses and now data lakes. A customized data management system aligns data processing with the company's regulations and provides exceptional decision-making support in real-time and scale. The following are some compelling case studies.

It is better to start with simpler and repetitive processes such as invoice processing. Such processes mostly rely on structured data inputs and have simpler workflows. This increases the chances of a successful deployment and gives you time and hand-on experience to understand the process closely.

 • Media and Entertainment

A combination of data warehouse and data lake helped a leading media multinational company with faster time-to-insight. It provided self-service access to several metrics including business KPIs, quality, and volume.

The solution provided to the company could ingest data from all the old and new sources. The data is fed directly into the data lake to create an enterprise-wide data catalog. It simplified data extraction, enrichment, refinement, and search.

With the augmentation of the data warehouse, the company moved critical datasets to a data lake that empowered data analytics and business intelligence teams to work independently of IT support for the preparation of data.

 • Casino and resort

A large casino and resort company wishes to leverage data analytics and data science to enhance its customer loyalty program. It intended to use current and future data to holistically optimize its program.

We delivered a quick solution by creating a new database management framework and ingest the enterprise data from RDBMS into it within weeks. We tracked the metrics of every ingest for faster and wider analytics. The data could be stored in raw format and accessed using self-service analytics in near real-time. The system could also provide predictive analytics that took the customers’ experience to the next level. Scalability to address future needs could be achieved without further development.

•  Financial analytics

A big data solution is provided to analytics providers that deal in data, analytics, benchmarks, and ratings. The company needed advanced technologies to provide real-time analytics for the real estate industry.

The massive data from various sources across the industry is ingested into the data lake and can be pushed to the data warehouse to allow reporting through traditional systems. The company was able to migrate millions of rows with over a hundred columns and handle the massive data that grows by nearly 10% each month. This enabled more informed analytics and accurate prediction in the industry, providing improved credibility to the company.

√  Traversing the future of database technologies

Data lakes and data warehouses are two different tools serving different purposes. Depending on the objective of the company they are used collaboratively to augment and complement each other’s capabilities. While the data warehouse holds cleaned and structured data, data lake contains all data. The data warehouse has a comparatively rigid structure whereas data lake can be easily modified and is cost-effective.

Despite their difference, these are two sides of the same coin. Glorious Insight understands the functional aspect of both the technologies and creates a database architecture for you that draws the best functionalities of both.

Companies are adopting the data-first strategy and are inclined towards data analytics-driven digital transformation like never before. The augmentation of database technologies helps in filling the gaps and overcoming the issues with individual implementation.

Even as the shift towards the implementation of warehouses and lakes is in progress, it has become a standard requirement for the businesses. It has given rise to a new kind of service called data warehouse as a service (DWaaS) which is predicted to cover 10 to 15% market by 2025.

If you are looking to cope with and lead the way into ever-evolving data analytics, having an augmented database architecture is inevitable. There is a lot of positivity about such a structure to achieve advanced data analytics and companies of all sizes and spectrums are going to make the shift very soon.