The different types of databases include operational databases, end-user databases, distributed databases, analytical databases, relational databases, hierarchical databases and database models. Databases are classified according to their type of content, application area and technical aspect. For instance, a deductive database combines logic programming with a relational database, while a graph database uses graph structures to represent and store information.
Other types of databases include hypertext databases, mobile databases, parallel databases, active databases, cloud databases, in-memory databases, spatial databases, temporal databases, real-time databases, probabilistic databases and embedded databases.
A database is an organized collection of data. Its primary function is to interact with a database management system to capture and analyze data. A database management system is a software system designed to allow the creation, querying and administration of databases. Some popular database management systems include PostgreSQL, MySQL, Microsoft SQL Server, Oracle, IBM DB2 and SAP.
Databases are designed to operate large amounts of information by inputting, storing, retrieving and managing it. They are set up in a way that allows users to easily and intuitively gain access to all the information. A database management maintains the integrity and security of stored data. It is also used for data recovery, in case of system failure.
ETL tools are designed to save time and money by eliminating the need of ‘hand-coding’ when a new data warehouse is developed. They are also used to facilitate the work of the database administrators who connect different branches of databases as well as integrate or change the existing databases.
The main purpose of the ETL tool is:
- extraction of the data from legacy sources (usually heterogenous)
- data transformation (data optimized for transaction –> data optimized for analysis)
- synchronization and cleansing of the data
- loading the data into data warehouse.
There are several requirements that must be had by ETL tools in order to deliver an optimal value to users, supporting a full range of possible scenarios.
– data delivery and transformation capabilities
– data and metadata modelling capabilities
– data source and target support
– data governance capability
– runtime platform capabilities
– operations and administration capabilities
– service-enablements capability.
ETL TOOLS COMPARISON CRITERIA
The bietltools.com portal is not affiliated with any of the companies listed below in the comparison.
The research inclusion and exclusion criteria are as follows:
– range and mode of connectivity/adapter support
– data transformation and delivery modes support
– metadata and data modelling support
– design, development and data governance support
– runtime platform support
– enablement of service and three additional requirements for vendors:
– $20 milion or more of software revenue from data integration tools every year or not less than 300 production customers
– support of customers in not less than two major geographic regions
– have customer implementations at crossdepartamental and multiproject level.
We miss a few etl tools, but think generally. Of course in world we have lots of etl tools, but for now we couldnt investigate which one is we miss.
ETL TOOLS COMPARISON
The information provided below lists major strengths and weaknesses of the most popular ETL vendors.
IBM (Information Server Infosphere platform)
- strongest vision on the market, flexibility
- progress towards common metadata platform
- high level of satisfaction from clients and a variety of initiatives
- difficult learning curve
- long implementation cycles
- became very heavy (lots of GBs) with version 8.x and requires a lot of processing power
- most substantial size and resources on the market of data integration tools vendors
- consistent track record, solid technology, straightforward learning curve, ability to address real-time data integration schemes
- Informatica is highly specialized in ETL and Data Integration and focuses on those topics, not on BI as a whole
- focus on B2B data exchange
- several partnerships diminishing the value of technologies
- limited experience in the field.
Microsoft (SQL Server Integration Services)
- broad documentation and support, best practices to data warehouses
- ease and speed of implementation
- standardized data integration
- real-time, message-based capabilities
- relatively low cost – excellent support and distribution model
- problems in non-Windows environments. Takes over all Microsoft Windows limitations.
- unclear vision and strategy
Oracle (OWB and ODI)
- based on Oracle Warehouse Builder and Oracle Data Integrator – two very powerful tools;
- tight connection to all Oracle datawarehousing applications;
- tendency to integrate all tools into one application and one environment.
- focus on ETL solutions, rather than in an open context of data management;
- tools are used mostly for batch-oriented work, transformation rather than real-time processes or federation data delivery;
- long-awaited bond between OWB and ODI brought only promises – customers confused in the functionality area and the future is uncertain
SAP BusinessObjects (Data Integrator / Data Services)
- integration with SAP
- SAP Business Objects created a firm company determined to stir the market;
- Good data modeling and data-management support;
- SAP Business Objects provides tools for data mining and quality; profiling due to many acquisitions of other companies.
- Quick learning curve and ease of use
- SAP Business Objects is seen as two different companies
- Uncertain future. Controversy over deciding which method of delivering data integration to use (SAP BW or BODI).
- BusinessObjects Data Integrator (Data Services) may not be seen as a stand-alone capable application to some organizations.
- experienced company, great support and most of all very powerful data integration tool with lots of multi-management features
- can work on many operating systems and gather data through number of sources – very flexible
- great support for the business-class companies as well for those medium and minor ones
- misplaced sales force, company is not well recognized
- SAS has to extend influences to reach non-BI community
- Data integration tools are a part of huge Java Composite Application Platform Suite – very flexible with ongoing development of the products
- ‘Single-view’ services draw together data from variety of sources; small set of vendors with a strong vision
- relative weakness in bulk data movement
- limited mindshare in the market
- support and services rated below adequate
- assembled a range of capabilities to be able to address a mulitude of data delivery styles
- size and global presence of Sybase create opportunities in the market
- pragmatic near-term strategy – better of current market demand
- broad partnerships with other data quality and data integration tools vendors
- falls behind market leaders and large vendors
- gaps in many aspects of data management
- functionality; well-known brand on the market (40 years experience); loyal customer and experience base;
- easy implementation, strong performance, targeted functionality and lower costs
- struggle with gaining mind share in the market
- lack of support for other than ETL delivery styles
- unsatisfactory with lack of capability of professional services
- message-oriented application integration; capabilities based on common SOA structures;
- support for federated views; easy implementation, support andperformance
- scarce references from customers; not widely enough recognised for data integration competencies
- lacking in data quality capabilities.
- proven and mature code-generating architecture
- one of the earliest vendors on the data integration market; support for SOA service-oriented deployments;
- successfully deals with large data volumes and a high degree of complexity, extension of the range of data platforms and data sources;
- customers’ positive responses to ETI technology
- relatively slow growth of customer base
- rather not attractive and inventive technology.
- offers physical data movement and delivery; support of wide range of adapters and access to numerous sources;
- well integrated, standard tools;
- reasonable ease of implementation effort
- gaps in specific capabilities
- relatively costly – not competitive versus market leaders
- many customers, years of experience, solid applications and support;
- good use of metadata
- upgrade from older versions into newer is straightforward.
- inconsistency in defining the target for their applications;
- no federation capability;
- limitated presence due to poor marketing.
- Simplicity of use in less-structured sources
- Easy licensing for business solutions
- cooperates with a wide range of sources and targets
- increasingly high functionality
- limited federation, replication and data quality support; rare upgrades due to its simplicity;
- weak real-time support due to use third party solutions and other database utilities.
Pitney Bowes Software
- Data Flow concentrates on data integrity and quality;
- supports mainly ETL patterns; can be used for other purposes too;
- ease of use, fast implementation, specific ETL functionality.
- rare competition with other major companies, repeated rebranding trigger suspicions among customers.
- narrow vision of possibilities even though Data Flow comes with variety of applications.
- weak support, unexperienced service.
I used referenced web site : etltools.net
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