Analytics

Understanding persistence in Apache Spark

Reading Time: 4 minutes In this blog, we will try to understand the concept of Persistence in Apache Spark in a very layman term with scenario-based examples. Note: The scenarios are only meant for your easy understanding. Spark Architecture Note: Cache memory can be shared between Executors. What does it mean by persisting/caching an RDD? Spark RDD persistence is an optimization technique which saves the result of RDD evaluation Continue Reading

Spark Structured Streaming (Part 4) – Handling Late Data

Reading Time: 3 minutes Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog “Understanding Stateful Streaming“. And this blog pertains to Handling Late Arriving Data in Spark Structured Streaming. So let’s get started. Handling Late Data With window aggregates (discussed in the previous blog) Spark automatically takes cares of late data. Every aggregate window is like a bucket Continue Reading

Spark Structured Streaming (Part 3) – Stateful Streaming

Reading Time: 4 minutes Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog “Internals of Structured Streaming“. And this blog pertains to Stateful Streaming in Spark Structured Streaming. So let’s get started. Let’s start from the very basic understanding of what is Stateful Stream Processing. But to understand that, let’s first understand what Stateless Stream Processing is. In Continue Reading

Spark Structured Streaming (Part 2) – The Internals

Reading Time: 2 minutes Welcome back folks to this blog series of Spark Structured Streaming. This blog is the continuation of the earlier blog “Introduction to Structured Streaming“. So I’ll exactly start from the point where I left in the previous blog. Structure of Streaming Query When we call start() API, Spark internally translates this code into a Logical Plan (an abstract representation of what the code does), then Continue Reading

Spark Structured Streaming (Part 1) – Introduction

Reading Time: 5 minutes In this Spark Structured Streaming series of blogs, we will have a deep look into what structured streaming is in a very layman language. So let’s get started. Introduction Structured streaming is a stream processing engine built on top of the Spark SQL engine and uses the Spark SQL APIs. It is fast, scalable and fault-tolerant. It provides rich, unified and high-level APIs in the Continue Reading

KSnow: Know about Cloning in Snowflake

Reading Time: 2 minutes This blog pertains to Cloning feature in Snowflake, and I will explain you all the things you need to know about these features with practical example. So let’s get started. Zero Copy Clone Cloning also Snowflake as Zero Copy Clone in Snowflake. It used to create a copy of a Table or Schema or a Database. In most database, in order to make a copy Continue Reading

Product demand forecasting with Knime

Reading Time: 5 minutes In this blog, we are going to see, Importance of demand forecasting and how we can easily create these forecasting workflows with Knime. Market request forecasting is a basic procedure for any business, however maybe none more so than those in buyer packaged products. Stock, production, storage, delivering, showcasing – each aspect of CPG and retail organizations’ activities are influenced by accurate forecasting. Identifying shoppers’ Continue Reading

KSnow: Time Travel and Fail-safe in Snowflake

Reading Time: 5 minutes This blog pertains to Time Travel and Fail-safe in Snowflake, and I will explain you all the things you need to know about these features with practical example. So let’s get started. Introduction to Time Travel Snowflake allows accessing historical data of a point in the past that may have been modified or deleted at the current time. Using time travel functionality a number of Continue Reading

Knime: Accessing a REST API with dynamic query param

Reading Time: 3 minutes Nowadays Rest API is the most widely used way to share data, In which many API returns a subset of complete data in form of page. Sometimes we need to append multiple query param in the URL to get some specific and filtered data. In this blog, we will learn how to generate dynamic URLs by adding query param and get data. Knime platform supports Continue Reading

MachineX: Run ML model prediction faster with Hummingbird

Reading Time: 3 minutes In this blog, we will see how to make our machine learning model’s prediction faster with a recently open-sourced library Hummingbird. Nowadays, we can see a lot of frameworks for deploying or serving the machine learning model into production. As a result, It is a headache for a data scientist to choose between these frameworks, keeping in mind how their model either Sklearn or LightGBM Continue Reading

KSnow: Load continuous data into Snowflake using Snowpipe

Reading Time: 5 minutes In this blog, we will discuss loading streaming data into Snowflake table using Snowpipe. But before that, if you haven’t read the previous part of this blog i.e., Loading Bulk Data into Snowflake then I would suggest you go through it. As now we have been set so let’s get started and see what Snowpipe is all about. Introduction Snowpipe is a mechanism provided by Continue Reading

KSnow: Loading Data Into Snowflake

Reading Time: 5 minutes This blog pertains to Loading Data into Snowflake, and I will explain you about the various step involved in this process. So let’s get started. Before moving ahead, you can visit the blog on understanding the basic of Snowflake Data Warehouse in case you want to refresh your concepts. Now let’s talk about the actual topic for which you have click on this blog. To Continue Reading