Why Modernize your SQL Server Data Warehouse? SQL Server data warehouses typically utilize SQL Server for database, SQL Server Integration Service (SSIS) for data integration, SQL Server Reporting Service (SSRS) for BI reports, and SQL Server Analytics Service (SSAS) for analytical needs. For legacy data warehouses developed with end-of-support versions of SQL Server, maintenance costs … Read more
What are Databricks, Microsoft Fabric and Snowflake? Databricks, Microsoft Fabric and Snowflake are all prominent data and AI platforms with a large presence in the analytics market. While most data professionals may be familiar with these leading platforms, here is a quick overview of each. Databricks Databricks has gained popularity as a platform of choice … Read more
Machine Learning Use Case Development The race is on for companies of all sizes and industries to deliver impactful artificial intelligence and machine learning applications. Fueled by the hype and fear of being left behind, many business leaders are throwing money and resources at implementing AI/ML use cases without having a clear understanding of a … Read more
PL/SQL versus Synapse and Snowflake PL/SQL is a procedural language designed to be embedded in SQL statements. It is a powerful language that can be used to perform a wide range of tasks, including data manipulation, error handling, and complex logic. However, PL/SQL can also be difficult to maintain and update, especially for large and … Read more
Overview of Microsoft Fabric Microsoft Fabric is an AI-powered analytics platform that integrates the best of Microsoft’s data and analytics tools, including Azure Data Factory, Azure Synapse Analytics, and Power BI. It offers a unified experience that enables organizations to seamlessly connect, prepare, analyze, and visualize data. Fabric allows users across the organization to leverage … Read more
Minimizing resolution time of any problems impacting your critical applications is key to maintaining operations and keeping end users and consumers happy. Delays in time-to-production such as errors with data pipelines or issues resulting in data inconsistencies can result in increased costs or even lost revenue. Machine learning (ML) can play a major role as it offers a paradigm shift in data platform monitoring. By analyzing historical data and identifying patterns, ML models can proactively predict and prevent potential problems.
The modern business landscape thrives on data-driven insights. But what if your data is trapped in outdated legacy systems, hindering your ability to analyze and utilize the data effectively? This is where data modernization can help ensure that your data is ready for modern analytics and AI requirements by transforming legacy ETL, data objects and orchestration in cloud-native architectures. Bitwise developed a detailed eBook on Data Modernization: Cloud-Native Architecture Transformation of ETL, Data Objects and Orchestration to guide data leaders through the benefits, challenges and solutions of modernizing data platform in the cloud. To provide a quick overview with the essential details of the eBook, we prepared this blog for a snapshot of cloud-native architecture transformation of ETL. Let’s take a look.
Legacy ETL systems, while robust and familiar, often lack the scalability, agility, and cloud-native capabilities of modern solutions. This can hinder your organization’s ability to leverage data for competitive advantage. Migrating your legacy ETL to Informatica Intelligent Data Management Cloud (IDMC) offers a compelling way to modernize your data integration landscape and ensure your data is ready to drive AI innovation.
Let’s take a look at Informatica’s IDMC and explore what you need to know when planning to migrate your ETL from legacy tools like DataStage, Ab Initio, SSIS and PowerCenter.
PySpark programmatical ETL versus GUI-based ETL PySpark programmatic ETL and GUI-based ETL are two different approaches to ETL (Extract, Transform, and Load).PySpark programmatic ETL involves writing ETL code in PySpark, a popular open-source distributed computing framework. This approach offers a number of advantages over GUI-based ETL tools, including: GUI-based ETL tools provide a … Read more
In today’s data-driven world, organizations are constantly striving to extract meaningful insights from their ever-expanding datasets. To achieve this, they need robust platforms that can seamlessly handle the complexities of data processing, storage, and analytics. In this blog, we’ll delve into the concept of data lakehouse that has emerged to address these challenges along with data warehouse.
