Data Engineering

Data Lake vs Delta Lake: Which is Better for Your Data Strategy?

July 31st, 2024 By Martina Šestak in Data Engineering

The fast-growing pace of big data volumes produced by modern data-driven systems often drives the development of big data tools and environments that aim to support data professionals in efficiently…

Iceberg Architecture Examples: How Iceberg powers data and ML applications

July 25th, 2024 By Radhika Gholap in Data Engineering

In recent years, Apache Iceberg has seen considerable advancements that highlights its growing importance. Major tech companies like Google, Snowflake, and Databricks have increasingly embraced this table format. Google integrated…

How are Apache Iceberg Tables Optimizing Data Lake Management?

July 25th, 2024 By Rahul Thakor in Data Engineering

A data lake is a central storage place for an organization's data in its original format. Unlike data warehouses, data lakes can handle all kinds of data, including unstructured and…

Avro vs Parquet: Which File Format is Right for You?

July 24th, 2024 By Dipal Prajapati in Data Engineering

While working with huge amounts of data, Data serialization plays an important role in the performance of the system. Data Serialization converts complex data structures, such as graphs, trees, etc.,…

Data Warehouse vs Data Lake vs Data Lakehouse – Key Comparisons

July 23rd, 2024 By Gabriela Aleksandrova in Data Engineering

With the vast amount of data being collected today for various purposes, there is an increasing need to find the proper data storage, which also heavily depends on your specific…

Apache Iceberg vs Delta Lake – Key Differences

July 23rd, 2024 By Parvathy Ramakrishnan in Data Engineering

Businesses are increasingly investing in data lakehouses due to their reduced costs, streamlined workloads, support for real-time data processing, and better decision-making. The global data lakehouse market is estimated to…

Using Emerging Technologies to Address Data Lake Challenges

July 23rd, 2024 By Adedotun Adeboye in Data Engineering

The term “Data Lake” was first introduced by James Dixon in 2010 as a form of storage to cope with evolving data needs due to advancements in IT and IoT.…

A Deep Dive into Data Lakehouses

July 18th, 2024 By Ahmed Shaaban in Data Engineering

The term “Data Lakehouse” is quite common nowadays. The new concept promises to address the failures of data warehouses and data lakes and help support the workloads of both business…

Top 10 Leading Data Lake Tools in 2025: Choose the Right One

July 12th, 2024 By Talha in Data Engineering

Are you looking for a data lake tool that is scalable, cost-efficient, and accessible, can store your business’s historical data, and can help you perform intelligent analytics?  No worries. To…

How to Adapt Apache Iceberg for High-scale Streaming Data

July 4th, 2024 By Srujana Maddula in Data Engineering, Data Streaming

Real-time data requires processing and analytics tasks to be performed within seconds. Organizations often struggle to store and process this data quickly enough. High throughput, large volumes, different data formats,…

How RAG Architecture Can Enhance Your Application’s Data Retrieval

July 4th, 2024 By Raju Mandal in Data Engineering

Retrieval-augmented generation (RAG Architecture) enhances response generation of a Large Language Model (LLM) by incorporating external information retrieval. It searches a database for information beyond the model's pre-trained knowledge base,…

Databricks SQL: Everything to Know

July 3rd, 2024 By Sarthak Bhardwaj in Data Engineering

Databricks SQL is an efficient platform for querying and analyzing large datasets. Its SQL editor, interactive dashboards, and robust BI tool integration features can help you streamline data exploration and…

Oracle Real Time Replication: Simple Steps to Set Up

June 26th, 2024 By Talha in Data Engineering

Are you looking for a simple method to set up real-time replication for data in your Oracle database? If yes, you are in the right place. Real time replication is…

Manage Date/Time Values Using EXTRACT From Oracle Function

June 26th, 2024 By Dimple M K in Data Engineering

Effective data management requires accurate data capture, storage, processing, and analysis. Date and time values are critical in organizing and filtering data, providing a foundation for efficient data processing. Oracle’s…

Understanding Oracle GoldenGate: A Comprehensive 101 Guide

June 26th, 2024 By Skand Agrawal in Data Automation, Data Engineering

As your business grows, so does the complexity of your data ecosystem. In today's data-driven world, managing and integrating this massive volume of data is critical yet challenging. You need…

What is Data Streaming? A Comprehensive Guide 101

June 7th, 2024 By Pratik Dwivedi in Data Engineering, Data Streaming

Real-time data is the need of the hour for businesses to make timely decisions, especially in cases of fraud detection or customer behavior analysis. Relying on traditional batch processing is…

How to Setup Spark Real-time Streaming? 2 Easy Methods

June 7th, 2024 By Ishwarya M in Data Engineering

In today's data-driven world, a colossal amount of data is generated from sensors, IoT devices, social networks, online transactions, and more. In order to harness the power of continuously generated…

6 Must-know Advantages of Data Replication

June 7th, 2024 By Preetipadma Khandavilli in Data Engineering

An automated data replication process is particularly essential for data-driven organizations as they produce a colossal amount of digital information daily. However, the advantages of data replication are not limited…

Data Pipeline Architecture: Diagrams & Best Practices

June 7th, 2024 By Sharon Rithika in Data Engineering, Data Pipeline

Data pipelines play a vital role in modern data management. They connect various data sources within an organization to those who need them. The ability to move data quickly and…

Data Pipeline Lambda: A Comprehensive Guide 101

June 7th, 2024 By Skand Agrawal in Data Engineering, Data Pipeline

Organizations today process and transform a large amount of data with ETL (extract, load, and transform) pipelines. But, loading and transforming this big data is time-consuming. However, sometimes, you do…