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Break Down Data Silos and Unlock Trapped Data with ETL Extract-Transform-Load Data for Improved Decision-Making Summary

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Break Down Data Silos and Unlock Trapped Data with ETL Extract-Transform-Load Data for Improved Decision-Making Summary Break Down Data Silos and Unlock Trapped Data with ETL Extract-Transform-Load Data for Improved Decision-Making Summary Data is essential for the day-to-day operations of an enterprise. However, to harness and derive value from it, it is important to break data silos and ETL helps accomplish that! It extracts information from disparate systems, transforms it into the required format, and loads it on to a destination for reporting and analysis. Go through this eBook to get in-depth knowledge about the Extract-Transform-Load process. We’ll walk you through the basic concepts of ETL and the benefits of adopting this approach to optimize your data processes. Furthermore, we’ll give you a round-up of features that businesses should look for in an enterprise-grade, high-performance ETL tool. ETL: Unlocking Data Silos for Improved Decision-Making Table of Contents THE ETL TOOLKIT: GETTING STARTED WITH THE BASICS ............................................. 04 What is ETL? 05 ETL Process Implementation: The Three Steps 05 Challenges of ETL 07 2SWLPL]H%XVLQHVV3URFHVVHVZLWK(7/ 08 ETL VS. ELT: A COMPARISON........................................................................................... 09 What is ELT? 10 ETL and ELT: Comparing Two Data Integration Approaches 10 Key Takeaway 12 GOING DEEPER: UNDERSTANDING ETL PROCESSES...................................................... 13 (7/DQG'DWDΖQWHJUDWLRQ:KDW$UHWKH'L΍HUHQFHV" 14 'L΍HUHQFHEHWZHHQ(7/3LSHOLQHVDQG'DWD3LSHOLQHV 14 )DFWRUV$΍HFWLQJ(7/3URFHVVHV 15 ETL USE CASES................................................................................................................... 17 Enterprise Applications of ETL 18 SELECTING AN ETL TOOL FOR YOUR ENTERPRISE TECHNOLOGY STACK ................... 20 ETL Tool: What to Look for? 21 Astera Centerprise: An Automated, Scalable ETL Tool 22 CONCLUSION ................................................................................................................. 2 The ETL Toolkit: Getting Started with the Basics ETL: Unlocking Data Silos for Improved Decision-Making | 04 What is ETL? ETL (Extract, Transform and Load) is an integration process that extracts relevant information from raw data, converts it into a format that fulfils business requirements, and loads it into a target system. The extraction, transformation, and loading processes work together to create an optimized ETL pipeline that allows for efficient migration, cleansing, and enrichment of critical business data. ETL Process Implementation: The Three Steps When it comes to the implementation of the ETL process, the itinerary of tasks can be divvied into the full form of its acronym: 1. E – Extraction 2. T – Transformation 3. L – Loading Here’s how this process converts raw data into intelligible insights. ETL: Unlocking Data Silos for Improved Decision-Making | 05 Step 1: Extraction The primary step involves pulling data from the relevant sources and compiling it. These sources may include on-premise databases, CRM systems, marketing automation platforms, unstructured and structured files, cloud applications, or any other source system that stores enterprise data. Once all the critical information has been extracted, it will be available in varying structures and formats. This information will have to be organized in terms of date, size, and source to suit the transformation process. There is a certain level of consistency required in all the data so it can be fed into the system and converted in the next step. The complexity of this step can vary significantly, depending on data types, the volume of data, and data sources. Extraction Steps • Unearth data from relevant sources • Organize data to make it consistent Step 2: Transformation Data transformation is the second step of the ETL process. Here the compiled data is converted, reformatted, and cleansed in the staging area to be fed into the target database in the next step. The transformation step involves executing a series of functions and applying sets of rules to the extracted data, to convert it into a standard format to meet the schema requirements of the target database. The level of manipulation required in transformation depends solely on the data extracted and the business requirements. It includes everything from applying expressions to data quality rules. Transformation Steps • Convert data according to the business requirements • Reformat converted data to a standard format for compatibility • Cleanse irrelevant data from the datasets o Sort & filter data o Remove duplications o Translate where necessary ETL: Unlocking Data Silos for Improved Decision-Making | 06 Step 3: Loading The last step includes loading the transformed datasets into the target database. There are two ways to go about it: first is a nSQL insert routine that involves the manual insertion of each record in every row of your target database table. Second approach uses a process called bulk loading, reserved for massive loading of data. The SQL insert may be slow, but it conducts integrity checks with each entry. While the bulk load is suitable for large data volumes that are free of errors. Loading Step • Load well-transformed, clean datasets through bulk loading or SQL inserts Challenges of ETL Implementing reliable ETL processes in today’s world of massive and complex amounts of data is no easy feat. Here are some of the challenges that may come up during ETL implementation: Data volume: Today, data is growing exponentially in volume. And while some business systems need only incremental updates, others require a complete reload each time. ETL tools must scale for large amounts of both structured and unstructured (complex) data. Data speed: Businesses today always need to be connected to enable real-time business insights and decisions and share the same information both externally and internally. As business intelligence analysis moves toward real-time, data warehouses and data marts need to be refreshed more often and more quickly. This requires real-time as well as batch processing. Disparate sources: As information systems become more complex, the number of sources from which information must be extracted are growing. ETL software must have flexibility and connectivity to a wide range of systems, databases, files, and web services. Diverse targets: Business intelligence systems and data warehouses, marts, and stores all have different structures that require a breadth of data transformation capabilities. Transformations involved in ETL processes can be highly complex. Data needs to be aggregated, parsed, computed, statistically processed, and more. Business intelligence-specific transformations are also required, such as slowly changing dimensions. Often data integration projects deal with multiple data sources and therefore need to handle issue of having multiple keys in order to make sense of the combined data. ETL: Unlocking Data Silos for Improved Decision-Making | 07 Optimize Business Processes with ETL Improved BI and Reporting Poor data accessibility is a critical issue that can affect even the most well-designed reporting and analytics process. ETL tools make data readily available to the users who need it the most by simplifying the procedure of extraction, transformation, and loading. As a result of this enhanced accessibility, decision-makers can get their hands on more complete, accurate, and timely business intelligence (BI). ETL tools can also play a vital role in both predictive and prescriptive analytics processes, in which targeted records and datasets are used to drive future investments or planning. Higher ROI According to a report by International Data Corporation (IDC), implementing ETL data processing yielded a median five-year return on investment (ROI) of 112 percent with an average payback of 1.6 years. Around 54 percent of the businesses surveyed in this report had an ROI of 101 percent or more. If done right, ETL implementation can save businesses significant costs and generate higher revenue. Improved Performance An ETL process can streamline the development of any high-volume data architecture. Today, numerous ETL tools are equipped with performance optimization technologies. Many of the leading solutions providers in this space augment their ETL technologies with high-performance caching and indexing functionalities, and SQL hint optimizers. They are also built to support multi-processor and multi-core hardware and thus increase throughput during ETL jobs. ETL: Unlocking Data Silos for Improved Decision-Making | 08 ETL vs. ELT: A Comparison ETL: Unlocking Data Silos for Improved Decision-Making | 09 What is ELT? ELT is an acronym for Extract, Load, and Transform. It’s a process that transfers raw data from the source to target system, and the information is then transformed within the subsequent system for downstream applications. This makes ELT most beneficial for handling enormous datasets, used for business intelligence and data analytics. EXTRACT LOAD TRANSFORM DATA WAREHOUSE SERVER DATA WAREHOUSE PUSHDOWN JOB ETL and ELT: Comparing Two Data Integration Approaches Whether you should use ETL or ELT for a data management use-case depends primarily on three things: the fundamental storage technology, your use case, and data architecture. To help you choose between the two, let’s discuss the advantages and drawbacks of each, one by one: ETL: Unlocking Data Silos for Improved Decision-Making | 10 Advantages of ETL • ETL can balance the capacity and share the amount of work with the relational database management system
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