Today, Gathr is excited to announce the launch of our new Confluent Cloud Connector, offering customers the ability to stream their data to and from Confluent Cloud in real time using Gathr. With today’s announcement, customers have a new way to manage their streaming data and derive value from it, 50X faster.
Category: XOps
Data + AI Summit 2023: A must-attend for data scientists, engineers, and business leaders
The Databricks Data + AI Summit is a premier event for data and AI professionals, featuring key industry leaders, innovative technologies, and emerging trends in the field. The annual event brings together thousands of data professionals and experts from around the world to share knowledge, insights, and experiences.
This year, the summit will be held from June 26-29, 2023, in San Francisco, California, USA. The event will be held both physically and virtually and is expected to draw thousands of attendees. Whether you’re a data engineer, data scientist, data analyst, or key decision maker, the summit offers tailored content to suit your role. This means you can expect to gain insights and practical tips on topics that are relevant to your daily work.
Here are some reasons why this event is a must-attend for anyone working in data, AI, and related fields:
Detect and prevent insider threats with real-time data processing and machine learning
Insider threats are one of the most significant cybersecurity risks to banks today. These threats are becoming more frequent, more difficult to detect, and more complicated to prevent. PwC’s 2018 Global Economic Crime and Fraud Survey reveals that people inside the organization commit 52% of all frauds. Information security breaches originating within a bank can include employees mishandling user credentials and account data, lack of system controls, responding to phishing emails, or regulatory violations.
Ignoring any internal security breach poses as much risk as an external threat such as hacking, especially in a highly regulated industry like banking. Some of the dangers of insider threats in the banking and financial industry include:
- Exposing the PII information of the customers
- Jeopardized customer relationship
- Fraud
- Loss of intellectual property
- Disruption to critical infrastructure
- Monetary loss
- Regulatory failure
- De-stabilized cyber assets of financial institutions
Identifying and fighting insider threats requires the capability to detect anomalous user behavior immediately and accurately. This detection presents its own set of challenges such as appropriately defining what is normal or malicious behavior and setting automated preventive controls to curb predicted threats.
Machine learning-based real-time threat detection for banks
The business impact of the COVID-19 pandemic continues to unfold worldwide for the financial services industry. The “new normal” has not only given rise to unprecedented operational challenges, but also provided fertile ground for hackers and threat actors to take advantage of increased vulnerabilities.
In June 2020, the Internet Crime Complaint Center at the FBI reported a 75% rise in daily digital crime since the start of stay-at-home restrictions. These cyber-crimes are not only becoming more frequent, but also more difficult to detect and more complicated to prevent. Financial institutions like banks that run hundreds of sensitive customer-facing applications are at extremely high risk.
Essentials for an enterprise data science solution
Data science solutions enable enterprises to explore their data, develop machine learning (ML) models, and operationalize them to drive business outcomes. Until recent years, data analysts and scientists had to switch from one tool to another to perform these steps, making the entire process of building models slow and cumbersome. However, modern data science solutions are changing the game by offering end-to-end workflows and advanced tools for all these processes.
Here is our take on 10 must-haves for a next-gen scalable enterprise data science solution.
To download the detailed checklist, click here.
Distributed Cloud: The Latest Innovation Accelerator
Now, in addition to public and private clouds, enterprises have the choice of combining cloud options into a distributed cloud network. In this article, Hari Kodakalla, Vice President, Cloud and Data Strategy, outlines how enterprises can adopt the distributed cloud with minimal risk and tap into its benefits to innovate, reduce time to insight, and improve the customer experience.
Read the complete article here Distributed Cloud: The Latest Innovation
Accelerator | Transforming Data with Intelligence (tdwi.org)
Data wrangling vs. data cleansing vs. ETL vs. ELT: Understanding key differences
With the growing complexity of analytics use cases, businesses are scrambling to derive actionable insights from unprecedented volumes of data pouring in from multiple sources. As the cloud landscape evolves rapidly, IT teams are under immense pressure to modernize their data management infrastructure and simplify time-consuming processes. As a result, there is a pressing need to deliver clean, accurate, and complete data at lightning speed for multiple analytics use cases.
Data preparation remains a major focus area, as it lays the foundation for advanced analytics. However, within data preparation, terms like data cleansing and data wrangling are often used interchangeably due to certain similarities. Some of the steps involved in these processes also overlap with ETL (extract, transform, load), leading to further confusion. Let’s take a closer look at the differences in these three processes and understand how each can help you maximize the potential of your data.
4 common data integration pitfalls to avoid
According to Gartner1, through 2024, 50% of organizations will adopt modern data quality solutions to better support their digital business initiatives. As enterprises work towards modernizing their data management infrastructure, data integration remains a key focus area. The data integration process brings together data from multiple systems, consolidates it and delivers it to a modern data warehouse or data lake for various analytical use cases. While there is no one approach to data integration, typical steps include data ingestion, preparation, and ETL (extract, transform, load). This blog outlines some of the most common data integration pitfalls and discusses strategies to avoid them.