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Data orchestration is the process of bringing data together from multiple sources, cleansing it, combining it, organizing it, and preparing it for analysis or operational use. The modern data journey can be seen as a collection of workflows. It starts with data extraction from different data sources, and continues with the transformation and consumption of the data. Every step on this journey can be seen as a separate data flow composed of smaller tasks. Data orchestration is the process of coordinating the execution and monitoring of these data processes.
Data professionals, including data analysts, data scientists and data engineers are responsible for creating data pipelines and workloads. This work has become a lot easier with the advancement of data orchestration software and technology that allows them to author, schedule, and monitor data pipelines programmatically. Today there are many data orchestration techniques that help automate data pipeline processes. These solutions combine, cleanse, and organize data from multiple sources, and direct the data to downstream services where other teams can put it to use. Understanding the various techniques and using the right one for each data source and type of usage is critical for efficient and timely access to critical information.
The key components of data orchestration are:
- Internal and External Data Integration: Most large enterprises are dealing with a large volume of big data in different storage systems, data warehouses, and data lakes. Connecting this amount of data coming from multiple providers is time-consuming and resource-intensive. Proper data orchestration connects data from different sources, stored in different places across the data ecosystem, and helps with data matching and harmonizing. Data matching can be a tedious task.
- Data Formatting: Datasets coming from different sources are often not in the same format, and need to be re-formatted or transformed according to business needs prior to use in any data projects.
Why is data orchestration important?
The purpose of data pipeline orchestration is to help a company easily consume data by connecting different systems and making sure that the most relevant data signals are ready to use, before they become outdated. Data orchestration is a core component of the modern data stack; it addresses several challenges in the data pipeline such as data errors, data matching, outdated data, and general data pipeline inefficiencies.
Without modern data orchestration capabilities engineers would have to spend too much time on integration and matching, taking away from tasks that create business value .
Data Orchestration for External Data
Data orchestration is traditionally engineering-heavy, but modernizing the data stack can free engineers for higher-value projects. When incorporating external data into data pipelines, an External Data Management Platform like Explorium provides a data orchestration solution, in addition to access to thousands of data signals. An External Data Platform is a newer addition to the modern data stack and is quickly becoming a necessity as the appetite for incorporating external or 3rd-party data increases. Most organizations today understand the value of external data; it has the power to boost data analytics and machine learning models and make predictive models more accurate. External data adds important context not found in internal data.
An External Data Management Platform is an enabling technology that automates and streamlines the steps required to effectively incorporate external data into your overall data and analytics strategy. For many organizations, the expected costs and delays are reason enough not to pursue new external data sources that could be vital for their decision making. A platform like Explorium makes data access and consumption easy through seamless 3rd party data integration into existing data pipelines.
Explorium is designed to connect with any BI and analytics solutions, data science platforms, and data visualization tools to help derive insights and predictions from the most relevant data enrichments. It provides several ways to connect external data into data pipelines for analytics and machine learning use cases.
Data Connectors: Directly import data from common data storage solutions, and export data to common data systems and downstream business applications. Upload from local files or data storage solutions by using pre-built connectors including Amazon S3, Google BigQuery, Snowflake, Microsoft Azure Blob, Teradata, SFTP, Postgres, and MySQL.
Exporting Data and Features: Export the enriched dataset and auto-generated machine learning features to a variety of data stores or connect them to analytics or predictive modeling solutions. The output connection is configured much like the input connection, based on the APIs of each solution (for example: create a timestamp file to send data to Microsoft Azure Blob and append the dataset to a table in Snowflake). Datasets are exported within an automated recipe upon a predefined schedule.
Open API: Explorium data and processing recipes are available via an open source API for integration with any 3rd party system. Customers use the API to query all the enrichments from Explorium into their analytics or BI solution.
Data Security: Explorium prioritizes security in every aspect of its products and processes and complies with the most rigid standards and regulations (ISO 27001, 27701 and 9001 certificates, SOC 2 Type 2 certificate, GDPR and CCPA compliant). All data is encrypted in transit and at rest. Traffic with the above connectors is encrypted using HTTPS protocol and TLS 1.2 (or higher).
It’s time to level up your data strategy
Leveraging the right types of data provides a competitive advantage. The data management process can be lengthy, detail oriented, and time-consuming. Luckily there are new technologies and tools that can help with an organization’s data needs including data management, data orchestration, data acquisition, and more. According to Gartner, “Data and analytics (D&A) leaders must envisage a strategy that empowers their practices to use small, wide, and synthetic data to drive business transformation via analytics augmented with AI and machine learning (ML).”
Learn more about Explorium’s External Data Platform and how it can help you level-up your data strategy by providing access to thousands of relevant data signals, and integrating them seamlessly into your existing data pipelines. Sign-up for a free trial today!
Explorium provides the first External Data Platform to improve data analytics and machine learning. Explorium enables the automation of data discovery to improve predictive ML model performance. Explorium External Data Platform empowers data scientists and analysts to acquire and integrate relevant external data signals efficiently, cost-effectively, and in compliance with regulations. With faster, better insights from their models, organizations across fintech, insurance, consumer goods, retail, and e-commerce can increase revenue, streamline operations and reduce risks. Learn more at www.explorium.ai.