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Top 5 Tools for Building an Interactive Analytics App

Smart Data Collective

Interactive analytics applications make it easy to get and build reports from large unstructured data sets fast and at scale. In this article, we’re going to look at the top 5. Firebolt makes engineering a sub-second analytics experience possible by delivering production-grade data applications & analytics. Google BigQuery.

Analytics 107
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11 Open-Source Data Engineering Tools Every Pro Should Use

ODSC - Open Data Science

This open-source streaming platform enables the handling of high-throughput data feeds, ensuring that data pipelines are efficient, reliable, and capable of handling massive volumes of data in real-time. Its open-source nature means it’s continually evolving, thanks to contributions from its user community.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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Discover the Snowflake Architecture With All its Pros and Cons- NIX United

Mlearning.ai

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

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What Are The Best Third-Party Data Ingestion Tools For Snowflake?

phData

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

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The Modern Data Stack Explained: What The Future Holds

Alation

In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds. What Is the Modern Data Stack? The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform.

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11 Open Source Data Exploration Tools You Need to Know in 2023

ODSC - Open Data Science

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. You can watch it on demand here.