Made in USA: Enterprise Application Services
Crunching big data in a data lake and/or data warehouse – Although the two have similarities, collecting and accessing data in a lake or warehouse environment differs in many ways. The types of data each accepts, and the ease of analysis are two major differences. Using either can result in better business intelligence but leveraging both best benefits a firm’s bottom line.
The term data lake refers to a centralized repository allowing structured and unstructured data storage at any scale. In a lake environment, information flows from line of business applications and non-relational sources like Internet of Things (IoT) devices, mobile applications and social media. Storing data as-is saves time. Despite its mishmash, a data lake still lets the user run analytics and access the information for big data processes and machine learning.
The term data warehouse refers to an optimized database designed for relational data analysis of information flowing from line of business applications and transactional systems. Fast SQL queries define the data schema and structure. Within the warehouse environment, the data gets cleaned, enriched and transmuted into report form. With analysis applied, the user accesses the final product in the form of operational reports.
Rather than choosing between a lake or warehouse environment, most organizations use both. Each serves a separate need.
Choosing the lake lets the user pick from a diverse range of queries. Unlike the warehouse, which requires establishment of SQL queries at its outset, the lake lets the user apply queries on the whole or to pieces and explore newly developed information models. Gartner calls it the Data Management Solution for Analytics (DMSA).
The warehouse proves a better choice for presentation of polished data. It efficiently processes relational data from line of business applications, operational databases and transactional systems.
Each choice has its positives and negatives. These include:
These lakes of data work better with newer analytics types like machine learning. A study by Aberdeen revealed that organizations leveraging a lake for data analysis experienced nine percent greater organic revenue growth than their peers. That’s because the lake let the business leaders identify and act on business growth opportunities more quickly by:
Not all lake and analytics platforms are created equally. Consider the following key capabilities when choosing a solution:
Using the lake methodology can lead to better customer interactions and research and development innovations plus an increase in operational efficiencies. This option lets you combine data from a various sources, including a CRM platform, incident tickets, a marketing platform and social media analytics to identify reasons for customer churn and potential options to increase customer loyalty. With respect to improved research and design, it can help test a hypothesis, reduce assumptions and analyze results. Finally, this option lets your organization increase operational efficiencies by allowing you to automatically collect, analyze and store real-time information from Internet of Things (IoT) devices.
Lakes do present certain challenges though. It contains a morass of raw data. It requires developing suitable security measures. It also needs defined queries or mechanisms created in order to conduct analysis. Before applying security measures and queries the lake is essentially a swamp. Lakes tend to be most easily implemented in the cloud. The cloud environment provides availability, performance, reliability and scalability. Cloud implementation also offers a faster deployment time, instant functionality updates and enhanced geographic coverage.
A warehouse can only amass and provide an analysis field for multiple heterogeneous sources. Its queries must be designed at the outset. Once incorporated into the warehouse, it can’t be changed. You can run analytics on historical data.
It also differs from a standard database, a transactional system monitoring and updating real-time data to provide the most recent data only. The warehouse aggregates structured data historically.
While lakes can accept any form of data and in raw format, a warehouse needs a specific structure. Follow these steps to create a warehouse for data.
This process produces well secured data that’s easily retrieved, reliable and manageable. Data stored in this manner can easily be mined. Business analysts use it to acquire insights to improve business processes. Warehouses make it simple for various departments to share data.
Implementing a warehouse for data represents a key component of a business intelligence program, says The Data Warehouse Institute. This centrally located, permanent home for business data allows access for all business intelligence functions from advanced analysis to reporting. Although they’re expensive, their key benefits justify the cost.
The warehouse coalesces data from multiple departments and enables inter-organizational sharing. Executives and managers can base decisions on analysis from a cohesive and up-to-date data set. This contributes to the elimination of uncertainty in business forecasting and reduces risk by providing improved data analysis that “can be applied directly to business processes including marketing segmentation, inventory management, financial management, and sales,” states BI Insider.
Warehousing data saves time in two key ways. It organizes critical data from a variety of sources and departments into one central pool. This eradicates the data gathering step. The warehouse environment also provides a simple querying method that executives can use themselves. This eradicates the need to involve the information technology in the generation of reports. This means management can conduct on the fly research of data during brainstorming sessions to accurately examine idea feasibility without a significant time investment.
The cleaning, sorting and conversion of data in a warehouse implementation turns data from numerous sources and systems into a common format. This standardization ensures reporting viability across departments. This highly accurate data provides a better source for business intelligence decisions.
A warehouse of data stores large amounts of historical data so you can analyze different time periods and trends in order to make future predictions. Such data typically cannot be stored in a transactional database or used to generate reports from a transactional system.
Examining the bottom line of firms that use the warehouse method combined with a complementary business intelligence system, they do provide a high return on investment (ROI). Those companies generated more revenue and saved more funds than firms with no warehoused data and business intelligence system.
Pick up the phone and call us to learn more about implementing a lake of data using Amazon Web Services (AWS) and warehousing data. Lakes provide the most comprehensive, cost-effective, scalable and secure service options. They let a firm build and analyze data in the AWS cloud. AWS already hosts customers like FINRA, iRobot, NASDAQ, Netflix and Zillow. Firms co-leverage a warehouse to enable a wider range of data analysis and ensure clean, optimized data that’s easy to analyze. Let us help you join those already leveraging the lake and the warehouse.