Database Scientist

  Blockchain Components Blockchain technology, as previously stated, is characterized by its blocks which are formed into chains, hence the name blockchain. However, blockchain technology is much more complicated than just a collection of blocks and chains. It requires many other components to actually build the block and make sure that they will not be tampered with and will remain safe. Some of these important technologies include cryptographic hash functions, asymmetric-key cryptography, and ledgers  [ 1 ] . The first main component of blockchain technology is the cryptographic hash functions. This is applied to data in a method called hashing. Hashing is a method used to calculate a unique output for an input of any size. The data is encrypted into a secure format which is unreadable unless the recipient has the keys. This allows individuals to take input data and hash the data to derive the same results. This proves that there has been no change to the data  [ 1...

  Stream processing is a big data technology that involves analyzing and processing continuous, high-volume data streams in real time, as the data is generated. Unlike traditional batch processing, which operates on finite, collected data at scheduled intervals, stream processing enables immediate insights and rapid, automated responses to events as they occur. Key ConceptsContinuous and Unbounded Data: Stream processing is designed for data that has a start but no defined end, such as data from IoT sensors, financial transactions, or social media feeds. Low Latency: The primary goal is minimal processing delay (milliseconds to seconds) to enable timely decision-making. Event Time vs. Processing Time: Stream processing systems can distinguish between when an event actually occurred (event time) and when the system processed it (processing time), which helps in handling out-of-order data or delays. Windowing: Unbounded data streams are often grouped into finite segments or "windows...

  The importance of Big Data Architecture is paramount in today’s day and age. Businesses have the capability to leverage distributed computing frameworks and cloud-based solutions to handle massive data volumes.   More importantly, the future scope of Big Data Architecture promises seamless integration of AI, edge computing, and  quantum computing , revolutioni sing industries and empowering data-driven decision-making for a brighter future. Explore this blog to learn in detail about Big Data Architecture, including its components, patterns, scaling methods and future scope. Read more.  Table of Content s   1) Understanding What is Big Data Architecture 2) Exploring the Core Components of Big Data Architecture 3) What are the Various Patterns in Big Data Architecture? 4) Benefits of Big Data Architecture 5) How can you Scale Big Data Architectures? 6) Looking at the Future Scope in Big Data Architecture   7) Conclusion Understanding  W hat is Big...

  Data is the backbone of each and every organization today. And this data, lying in siloes is of no use. Hence, integrating all of this siloed data is extremely important to actually do something out of that data. And to integrate this data, various methodologies have emerged. A few of them are ETL, ELT, Reverse ETL and Zero ETL. These methodologies are like the four options that you used to get in the Multiple Choice Questions (in probably the third grade?). Before any organization takes a decision of which one they must choose, it is crucial to understand what these methods are, how to use them and how they are different from each other- Difference between ETL and ELT is the most important out of all. They can’t be blindly filling out the circle of the OMR sheet like you used to do (in probably the third grade?). In this blog, we will explore these data integration techniques, highlighting their unique characteristics and appropriate use cases. ETL (Extract, Transform, Load) Wha...

  I have a bit of a cybersecurity background, so a year or two ago I started paying attention to how often data breaches happen - and I noticed something depressing: they happen literally 3+ times each day on average in the United States alone! (For a particularly sobering review check out this blog post.) All that information, your information, is out there, floating through the ether of the internet, along with much of my information: email addresses, old passwords, and phone numbers. Governments, countries, and individuals - with good reason - are taking notice and making changes. The GDPR was groundbreaking in its comprehension, but hardly alone in its goal to shape policy surrounding individual data rights. More policies will pass as the notions of individual privacy and data rights are explored, defined, and refined. Reconstruction Attacks A reconstruction attack is when an adversary gains access to the feature vectors that were used to train the ML model and uses them to rec...

  Overview of Time Series Databases A time-series database is usually used to deal with time-stamped or time-series data. It is also often considered a series of data studied over time. Time-stamped data would be any information collected over some time. For example, this could be any data, application performance monitoring, stock market trade records, book rental details from an old library, or an application's version management repository. However, a time series is built to handle time-stamped events and metrics. A time series database holds history with a finance application, but it has proved significant when managing real-time and historical data. The main difference between a time series and a normal database is that the queries are always made over time in a time series and not on average values. When plotted on a graph, a time series always has time as one axis. Why is Time Series DB important? This question arises because we already have very efficient working DB models....