Tue.Oct 17, 2023

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Watermark and input data filtering in Apache Spark Structured Streaming

Waitingforcode

I've already written about watermarks in a few places in the blog but despite that, I still find things to refresh. One of them is the watermark used to filter out the late data, which will be the topic of this blog post.

Data 130
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ChatGPT vs. BARD

KDnuggets

Large language models (LLMs) are transforming the way we process and produce information. But, before considering either one of these models as a one-stop-solution, one must consider their key differences.

Process 135
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Prepare your data for the National Spatial Reference System modernization of 2022 in the U.S.

ArcGIS

The new U.S. datums of 2022 will soon be released. This article covers what is coming and how you should prepare your data.

Systems 141
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The benefits of modern data architecture

InData Labs

Big data is central to the efficient running of all modern organizations, but to be of use, raw data must be suitably organized. The way that businesses organize data assets is commonly known as data architecture, with the benefits of modern data architecture enabling teams to respond to changing demands with improved agility when compared. Запись The benefits of modern data architecture впервые появилась InData Labs.

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Navigating the Future: Generative AI, Application Analytics, and Data

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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Sounds Like a Better Plan: USA Transportation Noise, Revised and Updated

ArcGIS

The Living Atlas of the World just updated the tiled, hosted image service featuring transportation noise, from the USDOT.

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Unlocking Reliable Generations through Chain-of-Verification: A Leap in Prompt Engineering

KDnuggets

Explore the Chain-of-Verification prompt engineering method, an important step towards reducing hallucinations in large language models, ensuring reliable and factual AI responses.

More Trending

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DALL·E 3 is Here with ChatGPT Integration

KDnuggets

Dive into how OpenAI’s new image generator DALL·E 3 is pushing limits, and see how it's making image generation much more accessible.

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Addressing the Challenges of Sample Ratio Mismatch in A/B Testing

DoorDash Engineering

Experimentation isn’t just a cornerstone for innovation and sound decision-making; it’s often referred to as the gold standard for problem-solving, thanks in part to its roots in the scientific method. The term itself conjures a sense of rigor, validity, and trust. Yet as powerful as experimentation is, its integrity can be compromised by overlooked details and unforeseen challenges.

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Real-Time Inventory in Retail with Confluent Cloud

Confluent

Use data streaming and stream processing (Flink, ksqlDB) to integrate data from store returns, purchases, exchanges, shipments, interstore transfers, etc., to produce a consistent, real-time view of inventory.

Retail 73
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Building a complete and composable CDP on the Lakehouse

databricks

Customer data is the lifeblood of modern organizations in every industry. As organizations level-up their data teams and practices with the Data Lakehouse.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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Some Kick Ass Prompt Engineering Techniques to Boost our LLM Models

KDnuggets

And how to go beyond its basics.

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Creating and Restoring from Snapshots in Rockset

Rockset

Data integrity is important and changes are often intimidating as they can disrupt data in unexpected ways. To make modifications less worrisome, Rockset now provides the ability to snapshot and restore collections. This will let users create a snapshot of a collection from which the collection can be restored in case the collection receives an unexpected modification.

SQL 52
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5 Lessons Learned from Testing Databricks SQL Serverless + DBT

Towards Data Science

We ran a $12K experiment to test the cost and performance of Serverless warehouses and dbt concurrent threads, and obtained unexpected results. By: Jeff Chou, Stewart Bryson Image by Los Muertos Crew Databricks’ SQL warehouse products are a compelling offering for companies looking to streamline their production SQL queries and warehouses. However, as usage scales up, the cost and performance of these systems become crucial to analyze.

SQL 52
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Snowflake Python UDFs: An overview

Cloudyard

Read Time: 2 Minute, 13 Second In this post, we are going to discuss Python User-Defined Functions (UDFs) within Snowflake. While we don’t have a specific use case in mind, this exploration is driven by the sheer potential these UDFs offer. Especially if we talk about the seamless integration of Python within Snowsight, this integration simplifies the coding experience.

Python 52
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How Embedded Analytics Gets You to Market Faster with a SAAS Offering

Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

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The Art of Master Data Management at Picnic

Picnic Engineering

Building the data foundation for world’s best online supermarket The Importance of MDM In the fast-paced world of technology, it’s easy to be captivated by the allure of artificial intelligence. As organizations seek to gain value out of data, AI models garner the spotlight, and a building block to unlock their potential is strong Master Data Management (MDM).

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Are you maximizing the potential of every shelf?

Retail Insight

If availability headaches and razor-thin margins are playing on your mind, optimizing store efficiency and increasing profits might seem like a tough challenge. However, at Retail Insight, we’ve drawn on our extensive grocery experience to help global retailers achieve the gold standard of retail execution and maximize the potential of every shelf. We call this ‘Shelf Actualization’.

Retail 52
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How to Achieve HIPAA Compliance for Your Organization?

Hevo

Does your organization need to utilize HIPAA-protected information? If part of your duty includes processing or handling any patient-related data, you may unknowingly violate HIPAA, for which the penalties can be severe. The violation of HIPAA compliance results in significant fines of up to $1.

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Automating product deprecation

Engineering at Meta

Systematic Code and Asset Removal Framework (SCARF) is Meta’s unused code and data deletion framework. SCARF guides engineers through deprecating a product safely and efficiently via an internal tool. SCARF combines this tooling with automation to reduce load on engineers. At Meta, we are constantly innovating and experimenting by building and shipping many different products, and those products comprise thousands of individual features.

Coding 112
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Understanding User Needs and Satisfying Them

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

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HIPAA Concerns in Cloud Data Warehouse- A Deep Dive

Hevo

Are you concerned that the data in a cloud data warehouse is not as secure as data stored on-premise? I agree that data stored on the cloud is prone to cyber-attacks even though they offer many features for ensuring data security.

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Bring Your Own Algorithm to Anomaly Detection

Pinterest Engineering

Charles Wu | Software Engineer; Isabel Tallam | Software Engineer; Kapil Bajaj | Engineering Manager Overview In this blog, we present a pragmatic way of integrating analytics, written in Python, with our distributed anomaly detection platform, written in Java. The approach here could be generalized to integrate processing done in one language/paradigm into a platform in another language/paradigm.

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ctrl+s Provides Granular Insights Into Supply Chain Sustainability With Snowflake’s Data Cloud

Snowflake

See how ctrl+s provides in-depth insights into supply chain sustainability, while protecting sensitive customer information—all through Snowflake’s powerful, scalable Data Cloud. Sustainability is an issue at the forefront of most companies’ agendas. But in complex, sprawling supply chains, identifying where carbon impacts come from can be extremely difficult.

Cloud 84
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5 Key Takeaways from #Current2023

Cloudera

Recently, Confluent hosted Current 2023 (formerly Kafka summit) in San Jose on Sept 26th and 27th. With few conferences curating content specific to streaming developers, Current has historically been an important event for anyone trying to keep a pulse on what’s happening in the streaming space. Over 2,000 attendees and lots of new solutions were on display, and the event proved to be a clear look into the current (no pun intended) state of streaming and where it is headed.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.