Big Data Data Ingestion Data Platform 
IBM Journey to AI blog
JANUARY 5, 2023
The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.
Pickl AI
APRIL 14, 2023
Falling into the wrong hands can lead to the illicit use of this data. Hence, adopting a Data Platform that assures complete data security and governance for an organization becomes paramount. In this blog, we are going to discuss more on What are Data platforms & Data Governance.
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AWS Machine Learning Blog
DECEMBER 18, 2023
In this post, we will explore the potential of using MongoDB’s time series data and SageMaker Canvas as a comprehensive solution. MongoDB Atlas MongoDB Atlas is a fully managed developer data platform that simplifies the deployment and scaling of MongoDB databases in the cloud.
Pickl AI
JUNE 29, 2023
It initiates the collection, indexing, and analysis of machine-generated data in real-time. It helps harness the power of big data and turn it into actionable intelligence. Moreover, it allows users to ingest data from different sources. Additionally, Splunk can process and index massive volumes of data.
Pickl AI
OCTOBER 10, 2024
Enhanced Data Quality : These tools ensure data consistency and accuracy, eliminating errors often occurring during manual transformation. Scalability : Whether handling small datasets or processing big data, transformation tools can easily scale to accommodate growing data volumes.
ODSC - Open Data Science
SEPTEMBER 29, 2023
But, the amount of data companies must manage is growing at a staggering rate. Research analyst firm Statista forecasts global data creation will hit 180 zettabytes by 2025. In our discussion, we cover the genesis of the HPCC Systems data lake platform and what makes it different from other big data solutions currently available.
The MLOps Blog
MARCH 15, 2023
A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.
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