Astera

Westlake Village, CA, USA
2002
  |  By Aisha Shahid
Data governance and data quality are closely related, but different concepts. The major difference lies in their respective objectives within an organization’s data management framework. Data quality is primarily concerned with the data’s condition. It ensures the data is complete, accurate, reliable, and consistent. On the other hand, data governance refers is the overall management, maintaining compliance, and ensuring the security of data assets within an organization.
  |  By Mariam Anwar
Data forms the foundation of the modern insurance industry, where every operation relies on digitized systems, including risk assessment, policy underwriting, customer service, and regulatory compliance. Given this reliance, insurance companies must process and manage data effectively to gain valuable insight, mitigate risks, and streamline operations.
  |  By Mariam Anwar
Every digital interaction generates data. This data can provide invaluable insights and drive effective decision-making when managed effectively. . However, according to a survey, up to 68% of data within an enterprise remains unused, representing an untapped resource for driving business growth. One way of unlocking this potential lies in two critical concepts: data governance and information governance.
  |  By Khurram Haider
A data quality framework is a set of guidelines that enable you to measure, improve, and maintain the quality of data in your organization. The goal is to ensure that organizational data meets specific standards, i.e., it is accurate, complete, consistent, relevant, and reliable at all times—from acquisition and storage to subsequent analysis and interpretation. eBook: A Guide to Data Quality Management Download eBook.
  |  By Abeeha Jaffery
Data completeness plays a pivotal role in the accuracy and reliability of insights derived from data, that ultimately guide strategic decision-making. This term encompasses having all the data, ensuring access to the right data in its entirety, to avoid biased or misinformed choices. Even a single missing or inaccurate data point can skew results, leading to misguided conclusions, potentially leading to losses or missed opportunities.
  |  By Aisha Shahid
Working with large volumes of data requires effective data management practices and tools, and two of the frequently used processes are data ingestion and ETL. Given the similarities between these two processes, non-technical people seek to understand what makes them different, often using search queries like “data ingestion vs ETL”.
  |  By Junaid Baig
A data catalog is a central inventory of organizational data. It provides a comprehensive view of all data assets in an organization, including databases, tables, files, and data sources. Efficiently managing large amounts of information is crucial for companies to stay competitive. This practice is especially applicable to large organizations with scattered data.
  |  By Aisha Shahid
Organizations rely on high-performance data warehouses for storing and analyzing large amounts of data. An important decision in setting up a data warehouse is the choice between Star Schema vs. Snowflake Schema. The star schema simplifies the structure of a database by directly connecting dimension tables to a central fact table. The star shaped design streamlines data retrieval and analysis by consolidating related data points, thereby enhancing the efficiency and clarity of database queries.
  |  By Aisha Shahid
Considering BigQuery vs. Redshift for your data warehousing needs? This guide is for you. Both BigQuery and Redshift stand as leading cloud data warehouse solutions each offering a multitude of features catering to multiple use cases. Google’s BigQuery offers seamless scalability and performance within its cloud platform, while Amazon’s Redshift provides great parallel processing and tuning options.
  |  By Usman Hasan Khan
According to a study by Statista, the cloud storage market was valued at $90.17 billion in 2022 and will reach a value of $472.47 billion by 2030. These figures indicate a growing shift toward cloud computing and data storage solutions. A typical scenario in modern data management involves data transfer from cloud storage to cloud-based computing platforms. Amazon’s Simple Storage Service (S3) is among the go-to options for the former, and businesses trust Snowflake for the latter.
  |  By Astera
In this video, we will see how Run SQL Script workflow task works in Astera Data Stack. The Run SQL Script task provides flexibility to write an SQL code within Astera and execute it as part of a workflow.
  |  By Astera
In this video, we will learn how to seamlessly integrate Salesforce databases into Astera Data Stack for efficient data extraction and loading. This video provides step-by-step guidance on configuring the Salesforce Database connector. Learn the process of establishing a successful connection and leveraging Salesforce data within your dataflows.
  |  By Astera
Discover Astera’s Data Pipeline Builder, the no-code solution for easy data integration in today's businesses. With its user-friendly drag-and-drop interface, integrating, cleaning, and transforming data has never been simpler. Watch our demo to see how Astera can automate your end-to-end data management lifecycle, boosting your organization's efficiency. Start watching to accelerate your data integration tasks!
  |  By Astera
Watch as we take you through the data preparation process for marketing data - step by step.
  |  By Astera
Astera EDI: Streamline Your EDI Mapping and Processing - Discover how Astera EDIConnect empowers businesses to seamlessly build, parse, and process EDI documents with trading partners without any coding required. Learn how our intuitive, no-code platform can automate your EDI transactions, ensuring data quality, security, and efficient partner communication. From healthcare to retail, see how industries benefit from our scalable, enterprise-ready EDI solution.
  |  By Astera
In this video, we will see how a Send Mail workflow task works in Astera Data Stack. A Send Mail task is used to send email notifications to users or administrators at certain points during a workflow job.
  |  By Astera
In this video, we will learn the functionality of the Run Workflow task within Astera Data Stack. Learn how to seamlessly integrate nested workflows, enabling the execution of multiple workflows within a single workflow. Discover how to configure the Run Workflow task to execute nested workflows sequentially or in parallel, optimizing workflow management and automation.
  |  By Astera
Join us on a journey towards creating a customer 360 marketing strategy. With Astera, you can seamlessly integrate data from various data sources and combine it into a single source of truth and make your data analysis ready.
  |  By Astera
In this video, we will be learning how to seamlessly integrate SQL code stored in a.SQL file into a workflow, automating database operations effortlessly by using the Run SQL File Task. The Run SQL File task runs the SQL code inside a file of.sql extension as part of a workflow.
  |  By Astera
In the insurance industry, the claims process plays a vital role in shaping an insurer's reputation, customer satisfaction, and financial performance. However, this process is primarily characterized by the substantial volumes of unstructured data that insurers must adeptly handle and leverage to enhance the customer journey and streamline claims lifecycle management.
  |  By Astera
The big increase in data, more sources of data, and the need for quick insights mean companies have to move away from slow, fixed methods of handling data. Dynamic ETL emerges as a timely solution, offering the flexibility to process data in real time, adapt to changing formats seamlessly, and scale operations efficiently.
  |  By Astera
The education sector has always worked with data to guide various processes, most notably student progress. But with powerful, AI-driven data extraction tools impacting other industries, it's time for educators to leverage these tools, accelerate data extraction, and turn data into actionable insights much faster.
  |  By Astera
A Single Customer View (SCV) is crucial for optimizing marketing ROI from a tech standpoint as it consolidates data from diverse channels, offering a complete customer profile.

Astera Software is a rapidly-growing provider of enterprise-ready data management solutions. Our goal is to make data-driven insights more accessible than ever through no-code, user-friendly, and automated data extraction, data integration, data warehousing, API management, and EDI solutions.

Features of Astera Centerprise:

  • Support for Diverse Systems: Connectivity to a range of structured, unstructured, and semi-structured data sources, including databases, web services, data warehouses, and flat file formats, such as delimited and CSV is the basic staple of all information mapping tools.
  • Graphical, Drag-and-Drop, Code-Free User Interface: A code-free environment to create mappings and a graphical, drag-and-drop UI to process data using built-in transformations.
  • Ability to Schedule and Automate Jobs: The ability to orchestrate a complete workflow using time and event-triggered job scheduling is a valuable feature in a tool. This automation cuts down the manual work, improving productivity and saving time.
  • Instant Preview Feature for Real-Time Testing: Intuitive features like Instant Data Preview help prevent mapping errors at the design time. This functionality lets the user view the processed and raw data at any step of the data process.
  • SmartMatch Data Conversion Mapping for Resolving Naming Conflicts: Synonym-driven file reading to resolve discrepancies in field names and business data lineage function to address the challenges of naming conflicts. It can be done by defining synonyms for a word in the synonym dictionary of a particular project.

Empowering Enterprises Across the Globe to Turn Data into Insights at Lightning-fast Speed!