In today’s digital data landscape, we are generating and accumulating huge variety, volume, and velocity of data. This is a major success in terms of insights which we can derive from the data to assist decision making. However, many organizations are still facing challenges in implementing a standardised process to store, prepare and analyze these data sets.
The solution is to seek a smarter Big Data management solution.
Data Warehouse modernization: A simple solution to big data management
In order to deal with these newer, high volume datasets, organizations are implementing enterprise-grade data lakes on advanced big data technologies like Hadoop, Amazon Redshift and Google BigQuery. These data lakes are used as parallel storage and processing platform to the existing data warehouse systems. With the new age advanced analytics tools, users can integrate and analyze their existing data warehouse with the new data lakes.
Preventing data overload
Another situation where data lakes can come to rescue when organizations are faced with either of the below situations:
Company’s’ current data warehouse cannot scale to support the amount of data that is being recorded
Handling unstructured data received from sources like social media, machine logs, sensors, web sources etc., which are cannot be handled by our underlying data warehouse.
The associated cost of storing and maintaining such datasets in existing warehouse system is high
Data lakes pave the path for unrestricted analytics and helps in capturing information which was not possible earlier due to data warehouse restrictions.
When used and applied correctly, organizations can see 3 key benefits:
- Data at your finger tips: Data lake makes current and historical data available for running analysis and thereby enabling business users to make more informative decisions.
- Centralized storage to all of your data: Analysts can integrate data coming from multiple data sources and of different structures and include them in their analysis. They can build correlations and patterns and derive deep dive insights to get a consolidated view of their enterprise.
- Faster analytics: The newer big data technologies are optimized for parallel processing and faster query response times on even peta-byte scale data. Compared to traditional data warehouses with which queries would run into hours and days, big data technologies ensure an in-time query performance.
Finding the right solution
Migrating data from a data warehouse to big data platform is easier said than done. It can very well be extremely expensive and time consuming, depending upon the technology you choose.
Following are the three data lake platforms that are seeing good traction currently:
Apache Spark and Hadoop
Apache Hadoop is an open source framework that excels in distributed storage and processing of big data with the ability to scale up to several petabytes of data. Apache spark processes data in-memory and enables batch, real time and advanced analytics on top of Hadoop. With a combination of Hadoop and Spark, organizations can store all-structure data, build data pipelines and analyze data at scale.
Redshift being a fully managed data warehouse solution allows users to run queries in sub-seconds to seconds of latency on their big data. Modern analytics tools can connect directly to Redshift and connect directly to Redshift.
The increase in the amount of data organizations are capturing and processing will continue to grow in coming years. To get the maximum benefits. Choosing the right big data strategy to get the most from your data is now critical.