Anzo Smart Data Manager FAQs
What is Anzo Smart Data Manager?
ASDM is the only enterprise data management software that uses an intelligent Excel-based interface to create reusable mappings between source/target systems and a conceptual model. Additionally, Anzo automatically compiles these mappings into ETL jobs that run on popular 3rd-party ETL engines and also tracks data lineage, business requirements, and other integration project governance details.
How does ASDM assist in Data Mapping?
Data Mapping within ASDM simplifies the linking and managing of data between source and target systems whether it be Hadoop, RDF or traditional data warehouses.
What is the ASDM Data Catalog?
The ASDM Data Catalog helps organizations understand where and what type of data is located in your data ecosystem.
Can ASDM help create ETL Jobs?
Yes, ASDM creates ETL Jobs that are immediately executable on popular open-source and commercial platforms such as Apache Spark, Pentaho or Informatica.
Can ASDM assist my IT organization with securely managing my data ecosystem?
Yes, Data Governance within ASDM tracks always-up-to-date data lineage information as an automated by-product of integrations.
How is the ASDM a game changing technology?
IDC estimates data is doubling every two years. Organizations need a solution that can manage, catalog and govern their ever growing big data at enterprise scale and security. ASDM allows companies to truly understand their data ecosystems and leverage the metadata within it.
Who is ASDM for?
Data Architects - ASDM can assist Data Architects with cataloging all the data across the organization by creating mappings of source and target data. Additionally, IT can generate ETL jobs from the mapping to be run in Apache Spark, Pentaho, or Informatica.
C Level - The Chief Data Officer and Chief Information Officers will now be able to fully understand what is in their data lakes or data ecosystems thus providing them with the knowledge and data security needed in regulated AND non-regulated industries.
How does ASDM compare to current Data Integration solutions?
Current data integration solutions on the market only perform data integration. ASDM allows user to ingest data into data lakes and Smart Data Lakes along with cataloging your data, mapping the data, and create ETL jobs on the fly.
How long does ASDM take to implement?
We have customers that are using ASDM to catalog data and map data source within a few days of purchasing the product.
Do I need to be able to code to use ASDM?
No users do not need any programing skills to use ASDM. Additionally, ASDM will compile the code for you to export the ETL jobs in Apache Spark.
What ETL compilers are supported?
ASDM supports Apache Spark, Pentaho and Informatica compilers to push out ETL jobs.
Can ASDM work with my existing Data Lake?
Yes, ASDM was created to assist Data Architects and business user transform their data lakes and get a clear understanding of their data. Additionally, ASDM is a critical component of the ANZO Smart Data Lake which allow organization to ingest all their structured and unstructured data to ask any question on the fly.
Anzo Graph Query Engine FAQs
What is the Anzo Graph Query Engine?
The Anzo Graph Query Engine (AGQE) is a massively parallel in-memory graph analytic platform built for performing common and sophisticated enterprise analytics on RDF data. The AGQE has the flexibility, performance, ease-of-use, and scale that is required to harness the full potential of Big Data analytics.
What is a Graph database?
Graph databases provide a simple, standardized method of storing RDF data. The RDF data model and standard SPARQL query language facilitate flexible data integration and data discovery by enabling you to load complex, diverse data from a variety of sources and discover existing relationships or create new ones on the fly.
How do a Relational database and the Anzo Graph Query Engine differ?
Let's use the example of an umbrella store. The relational database may just contain different data tables that pertain to the type of umbrellas. A Graph database can bring together the relational database data tables on type of umbrellas along with the social media data on the umbrella customers to assist in determining who to sell umbrellas to as well as the weather data in order to find out when to sell umbrellas. It allows you to bring together all your data, not just the data in rows and columns.
How is this game changing technology?
Facilitation of data exchange over the web, while enabling rich connected analytics, are the core design principles behind the AGQE. Cambridge Semantics’ deep relational and parallel system design expertise led to this game-changing, high-ROI analytic engine that is:
- Purpose built for Big Data analytics
- An in-memory solution that delivers 10-100x+ load and query performance over other graph databases
- Standards-based: no proprietary languages to learn
- Easily scaled to hundreds of servers
- Installed in minutes
What companies are using AGQE?
Leading organizations in industries such as Financial Services, Healthcare, and Retail, who compete based on analytic-driven information, are turning to the AGQE to capitalize on this opportunity to maximize their analytic advantage in areas such as compliance, fraud detection, product development, and social network analysis and marketing.
How long does AGQE take to implement?
You can install the Anzo Graph Query Engine, load your data, and run analytics in minutes.
Do I have to have the Anzo Smart Data Lake to use AGQE?
No. You can install and use the Anzo Graph Query Engine separately.
Why is AGQE part of the Smart Data Lake?
AGQE brings the ability to provide interactive analysis on a large scale to billions of triples so users can bring in all their data either structured or unstructured.
I work with relational database now. Do I need to be a developer to use this product?
No. Business analysts who can write a simple SQL query can leverage the AGQE to query 100x more data than querying a relational database.
Can I work with structured and unstructured data with Anzo Graph Query Engine?
Yes, users can transform any type of data into RDF and then have the context and meaning to analyze all their data.