November Edition 2020
With the rise of the Internet of Things and the connected digital ecosystem, almost everything we touch in our daily lives is producing vast amounts of data in various formats and on a rapid scale. The ability to harness this data to cultivate actionable insights is what innovative companies must do to deliver the best client experience effectively. To do this, however, capturing or "ingesting" a large amount of data into a central repository, like an Enterprise Data Lake, is the first step before any analytics, predictive modeling, or reporting can happen in earnest. Data ingestion is the transportation of data from assorted sources to a storage medium where it can be accessed, used, and analyzed by an organization. The destination is typically a data warehouse, data mart, database, or a document store. Sources may be almost anything, including SaaS data, in-house apps, databases, spreadsheets, or even information scraped from the internet. The data ingestion layer is the backbone of any analytics architecture.
Equalum is the fastest data ingestion platform, relied upon by enterprises across industries to stream data to operational, real-time analytics seamlessly, and machine learning environments. Built for scalability and ease of use, Equalum ingests data in real-time, as it is created from any number of data sources. It processes and transforms the data before streaming it to any number of target applications or systems. Its technology harnesses the power of Apache Spark and Kafka, among other cutting-edge open source technologies, helping organizations rapidly accelerate past traditional CDC, ETL, or open-source implementations with a zero-coding approach, intuitive design, and minimal maintenance. Equalum is backed by long-time successful VC firms and serial entrepreneurs.
Revolutionary Data Ingestion Services and Solutions
Enterprise-Grade Data Ingestion: Equalum's enterprise-grade real-time data ingestion architecture provides an end-to-end solution for collecting, transforming, manipulating, and synchronizing data – helping organizations rapidly accelerate past traditional change data capture (CDC) and ETL tools. Equalum moves data (in real-time or batch), combining its unique data ingestion capabilities with the power of leading open source projects. Utilizing an intuitive, user-friendly interface, Equalum users can build and deploy new data pipelines in minutes instead of days or months. A fully no-coding approach complete with a drag-and-drop UI enables a wide range of technical and business users to configure, maintain, and derive insights. In addition to its native data ingestion modules, the platform leverages Apache Spark and Kafka's power, among other cutting-edge open source technologies valued for their scalability and innovation.
Change Data Capture (CDC) Ingestion: As data, volumes explode; fueled largely by the growth of business and machine data, and business users increasingly demand continuous access to insights, the extraction of all underlying data in real-time is no longer practical. Instead, solutions must be deployed to identify and monitor changes to critical data elements and stream those changes to a real-time analytics environment. Change data capture is the lowest-impact way of asynchronously capturing database changes. But legacy CDC solutions come with several limitations. They are built primarily for data replication and typically offer minimal data transformation capabilities, provide limited support for newer database technologies, and are priced for isolated replication scenarios rather than enterprise-wide use. Equalum's library of high-performing, out-of-the-box CDC tools leverage all relevant APIs to capture changes from any database or non-database source, transform and enrich the data in motion, and stream changes to a data warehouse or data lake.
Automated Data Ingestion for Data Lakes: Centralizing data from across the enterprise into public cloud data lakes or Apache Hadoop typically requires extensive custom coding and ongoing maintenance. Engineering teams must build connectors to enterprise applications (like ERPs or CRMs), extract data from operational databases in a low-footprint manner, load in data from file formats like XML or JSON, and manage unwieldy new data sources like equipment sensors and IoT control systems. Custom scripting is expensive and time-consuming, while batch ETL jobs fail under heavy loads and are not equipped to handle schema changes over time. Equalum makes it easy to replicate data from throughout the enterprise to data lakes like AWS, Microsoft Azure, Google Cloud Platform, and Apache Hadoop. Equalum uniquely combines its native technology with open sources' scalability, big data frameworks like Spark and Kafka to dramatically improve data pipelines' performance – enabling organizations to increase data volumes while reducing processing time.
Meet the Leader
Nir Livneh is the Founder and serves as the Chief Executive Officer of Equalum. He is a thought leader with over 20 years of experience in Big Data architecture and performance. He led product management at Quest Software (Acquired by Dell) for all Big Data products. Mr. Nir also led Big Data architecture projects at the Israeli Military Intelligence unit (Equivalent to NSA). He is currently a member of the Forbes Technology Council.