Algorithms and Data Structures for Massive Datasets by Dzejla Medjedovic and Emin Tahirovic.
Dzejla holds a PhD in Computer Science from Stony Brook University and currently serves as a Vice President of Data at Social Explorer. Emin holds a MS in Theoretical Computer Science from Goethe University and a PhD in Biostatistics from the University of Pennsylvania. He is a faculty member at the International University of Sarajevo. Together, Dzejla and Emin are leveraging their expertise in algorithms, data structures, and statistics to provide insights into managing massive datasets.
We now live is a world beyond big data. Today data lakes at petabyte and exabyte scale are common. Zettabyte cannot be very far away. Unlike traditional data warehouses, data lakes in fact accommodate structured, semi-structured, and unstructured data from various sources.
Dzejla and Emin are certainly providing a complex topic in a friendly manner to make complex concepts easy to understand. Readers will learn about deploying the correct type of database solution for your organization’s applications. This was a very enjoyable book to read.
Is your algorithm stretched?
They also reveal the evaluation and design data structures and algorithms. Perhaps many will learn about necessary algorithmic trade-offs in order to effectively manage your organization’s massive data. In fact, how your compute capacity will impact limited space resources within your current state infrastructure.
In fact at their core, massive datasets force traditional data structures and algorithms to falter. This is why deploying the right database engine for your applications is critical. Many organizations fail right out of the gate by continuing to leverage legacy database technologies. So your organization must be re-evaluating and implementing data structures and algorithms in order to successfully deploy responsive applications. Think Netflix, Google, Facebook and other companies who deploy data structures and algorithms and other enterprise applications that efficiently work with massive amounts of data.
In conclusion, this book serves as an important resource for organizations seeking to enhance their understanding of big data processing techniques. Readers will appreciate the efforts to make complex ideas practical. Dzejla and Emin reveal how to find a balance between saving space and maintaining data accuracy with massive datasets.