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The DataHour: Build Your First Chatbot Using Open Source Tools

Analytics Vidhya

Dear Readers, The latest edition of our flagship learning series on everything in and about data analytics is sure to excite your minds, be prepared for the DataHour on Building your First Chatbot using Open Source Tools.

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Choosing the Best WordPress Hosting for a Data-Intensive Website

Smart Data Collective

You need to make sure that you have access to the right data analytics and machine learning tools. Your website will operate a lot more seamlessly if you have the right big data technology at your disposal. If you use the WordPress CMS, then you are going to want to choose a hosting provider that can accommodate it.

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5 Things Data-Driven Companies Must Look for When Buying Web Hosting

Smart Data Collective

There are a lot of things that companies need to take into consideration when managing their data. One of the most important is selecting the right web hosting plan. The Importance of Selecting the Right Hosting Plan for Your Data-Centric Business. You need to use a website that takes advantage of big data.

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Corinium acquires RE•WORK

Corinium

Acquiring this complimentary portfolio of events contributes to Corinium’s rapid growth strategy, adding to its portfolio of tech-focused in-person, digital and hybrid events for data, analytics and digital innovation-focused executives. It also hosts the Women in AI dinner and Women in AI podcast series.

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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success.

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Three reasons to self-host your product analytics

KDnuggets

Want three reasons to avoid the cloud and host your own analytics platform? More data, more control, more secure.

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Modernize your legacy databases with AWS data lakes, Part 2: Build a data lake using AWS DMS data on Apache Iceberg

AWS Big Data

format(dbname, table_name)) except Exception as ex: print(ex) failed_table = {"table_name": table_name, "Reason": ex} unprocessed_tables.append(failed_table) def get_table_key(host, port, username, password, dbname): jdbc_url = "jdbc:sqlserver://{0}:{1};databaseName={2}".format(host, To start the job, choose Run. format(dbname)).config("spark.sql.catalog.glue_catalog.catalog-impl",

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